Title: Observability-based Rules for Designing Consistent EKF SLAM Estimators
Authors: Guoquan P. Huang, Anastasios Mourikis, and Stergios I. Roumeliotis
Abstract:
In this work, we study the inconsistency problem of extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) from the perspective of observability. We analytically prove that when the Jacobians of the process and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has an observable subspace of dimension higher than that of the actual, non-linear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in
directions of the state space where no information is available, which is a primary cause of the inconsistency. Based on these theoretical results, we propose a general framework for improving the consistency of EKF-based SLAM. In this framework, the EKF linearization points are selected in a way that ensures that the resulting linearized system model has an observable subspace of appropriate dimension. We describe two algorithms that are instances of this paradigm. In the first, termed observability constrained (OC)-EKF, the linearization points are selected so as to minimize their expected errors (i.e. the difference between the linearization point and the true state) under the observability constraints. In the second, the filter Jacobians are calculated using the first-ever available estimates for all state variables. This latter approach is termed first-estimates Jacobian (FEJ)-EKF. The proposed algorithms have been tested both in simulation and experimentally, and are shown to significantly outperform the standard EKF both in terms of accuracy and consistency.
Link:
The International Journal of Robotics Research(IJRR), Vol.5 April 2010
http://ijr.sagepub.com/content/29/5/502.full.pdf+html
Monday, February 28, 2011
Sunday, February 20, 2011
Wednesday, February 09, 2011
Lab Meeting February 14, 2011 (fish60): Feature Construction for Inverse Reinforcement Learning
Title: Feature Construction for Inverse Reinforcement Learning
Sergey Levine, Zoran Popović, Vladlen Koltun
NIPS 2010
Abstract:
The goal of inverse reinforcement learning is to find a reward function for a
Markov decision process, given example traces from its optimal policy. Current
IRL techniques generally rely on user-supplied features that form a concise basis
for the reward. We present an algorithm that instead constructs reward features
from a large collection of component features, by building logical conjunctions of
those component features that are relevant to the example policy. Given example
traces, the algorithm returns a reward function as well as the constructed features.
Link
Sergey Levine, Zoran Popović, Vladlen Koltun
NIPS 2010
Abstract:
The goal of inverse reinforcement learning is to find a reward function for a
Markov decision process, given example traces from its optimal policy. Current
IRL techniques generally rely on user-supplied features that form a concise basis
for the reward. We present an algorithm that instead constructs reward features
from a large collection of component features, by building logical conjunctions of
those component features that are relevant to the example policy. Given example
traces, the algorithm returns a reward function as well as the constructed features.
Link
Lab Meeting February 14, 2011 (Alan): Multibody Structure-from-Motion in Practice (PAMI 2010)
Title: Multibody Structure-from-Motion in Practice (PAMI 2010)
Authors: Kemal Egemen Ozden, Konrad Schindler, and Luc Van Gool
Abstract—Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring.
Link
Authors: Kemal Egemen Ozden, Konrad Schindler, and Luc Van Gool
Abstract—Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring.
Link
Monday, January 17, 2011
Lab Meeting January 17( KuenHan ), Moving Object Detection by Multi-View Geometric Techniques from a Single Camera Mounted Robot (IROS 2009)
Title: Moving Object Detection by Multi-View Geometric Techniques from a Single Camera Mounted Robot ( IROS 2009)
Author: Abhijit Kundu, K Madhava Krishna and Jayanthi Sivaswamy
Abstract:
The ability to detect, and track multiple moving
objects like person and other robots, is an important prerequisite
for mobile robots working in dynamic indoor environments.
We approach this problem by detecting independently moving
objects in image sequence from a monocular camera mounted
on a robot. We use multi-view geometric constraints to classify
a pixel as moving or static. The first constraint, we use, is the
epipolar constraint which requires images of static points to
lie on the corresponding epipolar lines in subsequent images.
In the second constraint, we use the knowledge of the robot
motion to estimate a bound in the position of image pixel along
the epipolar line. This is capable of detecting moving objects
followed by a moving camera in the same direction, a so-called
degenerate configuration where the epipolar constraint fails.
To classify the moving pixels robustly, a Bayesian framework
is used to assign a probability that the pixel is stationary
or dynamic based on the above geometric properties and
the probabilities are updated when the pixels are tracked in
subsequent images. The same framework also accounts for the
error in estimation of camera motion. Successful and repeatable
detection and pursuit of people and other moving objects in
realtime with a monocular camera mounted on the Pioneer
3DX, in a cluttered environment confirms the efficacy of the
method.
Link
Author: Abhijit Kundu, K Madhava Krishna and Jayanthi Sivaswamy
Abstract:
The ability to detect, and track multiple moving
objects like person and other robots, is an important prerequisite
for mobile robots working in dynamic indoor environments.
We approach this problem by detecting independently moving
objects in image sequence from a monocular camera mounted
on a robot. We use multi-view geometric constraints to classify
a pixel as moving or static. The first constraint, we use, is the
epipolar constraint which requires images of static points to
lie on the corresponding epipolar lines in subsequent images.
In the second constraint, we use the knowledge of the robot
motion to estimate a bound in the position of image pixel along
the epipolar line. This is capable of detecting moving objects
followed by a moving camera in the same direction, a so-called
degenerate configuration where the epipolar constraint fails.
To classify the moving pixels robustly, a Bayesian framework
is used to assign a probability that the pixel is stationary
or dynamic based on the above geometric properties and
the probabilities are updated when the pixels are tracked in
subsequent images. The same framework also accounts for the
error in estimation of camera motion. Successful and repeatable
detection and pursuit of people and other moving objects in
realtime with a monocular camera mounted on the Pioneer
3DX, in a cluttered environment confirms the efficacy of the
method.
Link
Sunday, January 09, 2011
Lab Meeting January 10th, 2011(Jimmy) : Accurate Image Localization Based on Google Maps Street View (ECCV 2010)
Title: Accurate Image Localization Based on Google Maps Street View
Authors: Amir Roshan Zamir, Mubarak Shah
In ECCV 2010
Abstract
Finding an image's exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of fi nding the GPS location of images with an accuracy which is comparable to hand-held GPS devices. We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is de fined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The fi nal location is determined based on the Confidence of Localization parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.
[pdf]
Authors: Amir Roshan Zamir, Mubarak Shah
In ECCV 2010
Abstract
Finding an image's exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of fi nding the GPS location of images with an accuracy which is comparable to hand-held GPS devices. We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is de fined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The fi nal location is determined based on the Confidence of Localization parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.
[pdf]
Monday, January 03, 2011
Lab Meeting January 3rd, 2011(Will) : Neural Prothesis & Realtime Bayes Tracking
Topic: Neural Prothesis & Realtime Bayes Tracking
Neural prothesis is a field that use brain to control motors to help disable people.
I'll report my survey on the neural prothesis decoding algorithm.
Po-Wei
Neural prothesis is a field that use brain to control motors to help disable people.
I'll report my survey on the neural prothesis decoding algorithm.
Po-Wei
Sunday, December 26, 2010
Lab Meeting January 3rd, 2011(David) :Vision-Based Behavior Prediction in Urban Traffic Environments by Scene Categorization (BMVC 2010)
Title: Vision-Based Behavior Prediction in Urban Traffic Environments by Scene Categorization (BMVC 2010)
Authors: Martin Heracles, Fernando Martinelli and Jannik Fritsch
Abstract:
We propose a method for vision-based scene understanding in urban traffic environments that predicts the appropriate behavior of a human driver in a given visual scene. The method relies on a decomposition of the visual scene into its constituent objects by image segmentation and uses segmentation-based features that represent both their identity and spatial properties. We show how the behavior prediction can be naturally formulated as scene categorization problem and how ground truth behavior data for learning a classifier can be automatically generated from any monocular video sequence recorded from a moving vehicle, using structure from motion techniques. We evaluate our method both quantitatively and qualitatively on the recently proposed CamVid dataset, predicting the appropriate velocity and yaw rate of the car as well as their appropriate change for both day and dusk sequences. In particular, we investigate the impact of the underlying segmentation and the number of behavior classes on the quality of these predictions
link
Authors: Martin Heracles, Fernando Martinelli and Jannik Fritsch
Abstract:
We propose a method for vision-based scene understanding in urban traffic environments that predicts the appropriate behavior of a human driver in a given visual scene. The method relies on a decomposition of the visual scene into its constituent objects by image segmentation and uses segmentation-based features that represent both their identity and spatial properties. We show how the behavior prediction can be naturally formulated as scene categorization problem and how ground truth behavior data for learning a classifier can be automatically generated from any monocular video sequence recorded from a moving vehicle, using structure from motion techniques. We evaluate our method both quantitatively and qualitatively on the recently proposed CamVid dataset, predicting the appropriate velocity and yaw rate of the car as well as their appropriate change for both day and dusk sequences. In particular, we investigate the impact of the underlying segmentation and the number of behavior classes on the quality of these predictions
link
Wednesday, December 22, 2010
Lab Meeting December 27, 2010(Chih Chung) : Lozano-Perez. Belief space planning assuming maximum likelihood observations.(RSS 2010)
Title:Belief space planning assuming maximum likelihood observations
Authors:Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez
Abstract:
We cast the partially observable control problem as
a fully observable underactuated stochastic control problem in
belief space and apply standard planning and control techniques.
One of the difficulties of belief space planning is modeling the
stochastic dynamics resulting from unknown future observations.
The core of our proposal is to define deterministic beliefsystem
dynamics based on an assumption that the maximum
likelihood observation (calculated just prior to the observation)
is always obtained. The stochastic effects of future observations
are modelled as Gaussian noise. Given this model of the dynamics,
two planning and control methods are applied. In the first, linear
quadratic regulation (LQR) is applied to generate policies in the
belief space. This approach is shown to be optimal for linear-
Gaussian systems. In the second, a planner is used to find locally
optimal plans in the belief space. We propose a replanning
approach that is shown to converge to the belief space goal
in a finite number of replanning steps. These approaches are
characterized in the context of a simple nonlinear manipulation
problem where a planar robot simultaneously locates and grasps
an object.
link
Authors:Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez
Abstract:
We cast the partially observable control problem as
a fully observable underactuated stochastic control problem in
belief space and apply standard planning and control techniques.
One of the difficulties of belief space planning is modeling the
stochastic dynamics resulting from unknown future observations.
The core of our proposal is to define deterministic beliefsystem
dynamics based on an assumption that the maximum
likelihood observation (calculated just prior to the observation)
is always obtained. The stochastic effects of future observations
are modelled as Gaussian noise. Given this model of the dynamics,
two planning and control methods are applied. In the first, linear
quadratic regulation (LQR) is applied to generate policies in the
belief space. This approach is shown to be optimal for linear-
Gaussian systems. In the second, a planner is used to find locally
optimal plans in the belief space. We propose a replanning
approach that is shown to converge to the belief space goal
in a finite number of replanning steps. These approaches are
characterized in the context of a simple nonlinear manipulation
problem where a planar robot simultaneously locates and grasps
an object.
link
Sunday, December 19, 2010
Lab Meeting December 20, 2010(Chung-Han): progress report
I will report my progress on ground-truth annotation.
Sunday, December 12, 2010
Lab Meeting December 13, 2010(ShaoChen): DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs(IROS2010)
Title: DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs
Authors: Alexander Cunningham, Manohar Paluri, and Frank Dellaert
Abstract:
We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.
[link]
Authors: Alexander Cunningham, Manohar Paluri, and Frank Dellaert
Abstract:
We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.
[link]
Monday, December 06, 2010
Lab Meeting December 6th, 2010(Nicole): Acoustic Source Localization and Tracking Using Track Before Detect
Title: Acoustic Source Localization and Tracking Using Track Before Detect
(IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, 2010)
Authors: Maurice F. Fallon, Simon Godsill
Abstract:
Particle Filter-based Acoustic Source Localization algorithms attempt to track the position of a sound source—one or more people speaking in a room—based on the current data from a microphone array as well as all previous data up to that point. This paper first discusses some of the inherent behavioral traits of the steered beamformer localization function. Using conclusions drawn from that study, a multitarget methodology for acoustic source tracking based on the Track Before Detect (TBD) framework is introduced. The algorithm also implicitly evaluates source activity using a variable appended to the state vector. Using the TBD methodology avoids the need to identify a set of source measurements and also allows for a vast increase in the number of particles used for a comparitive computational load which results in increased tracking stability in challenging recording environments. An evaluation of tracking performance is given using a set of real speech recordings with two simultaneously active speech sources.
[link]
(IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, 2010)
Authors: Maurice F. Fallon, Simon Godsill
Abstract:
Particle Filter-based Acoustic Source Localization algorithms attempt to track the position of a sound source—one or more people speaking in a room—based on the current data from a microphone array as well as all previous data up to that point. This paper first discusses some of the inherent behavioral traits of the steered beamformer localization function. Using conclusions drawn from that study, a multitarget methodology for acoustic source tracking based on the Track Before Detect (TBD) framework is introduced. The algorithm also implicitly evaluates source activity using a variable appended to the state vector. Using the TBD methodology avoids the need to identify a set of source measurements and also allows for a vast increase in the number of particles used for a comparitive computational load which results in increased tracking stability in challenging recording environments. An evaluation of tracking performance is given using a set of real speech recordings with two simultaneously active speech sources.
[link]
Lab Meeting December 6th, 2010(KuoHuei): progress report
I will present my progress on Neighboring Objects Interaction models.
Sunday, November 28, 2010
Lab Meeting November 29, 2010 (Wang Li): Adaptive Pose Priors for Pictorial Structures (CVPR 2010)
Adaptive Pose Priors for Pictorial Structures
Benjamin Sapp
Chris Jordan
Ben Taskar
Abstract
The structure and parameterization of a pictorial structure model is often restricted by assuming tree dependency structure and unimodal, data-independent pairwise interactions, which fail to capture important patterns in the data. On the other hand, local methods such as kernel density estimation provide nonparametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset), while using only 15% of the training data as exemplars.
Paper Link
Benjamin Sapp
Chris Jordan
Ben Taskar
Abstract
The structure and parameterization of a pictorial structure model is often restricted by assuming tree dependency structure and unimodal, data-independent pairwise interactions, which fail to capture important patterns in the data. On the other hand, local methods such as kernel density estimation provide nonparametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset), while using only 15% of the training data as exemplars.
Paper Link
Saturday, November 27, 2010
Lab Meeting November 29th, 2010 (Jeff): Sub-Meter Indoor Localization in Unmodified Environments with Inexpensive Sensors
Title: Sub-Meter Indoor Localization in Unmodified Environments with Inexpensive Sensors
Authors: Morgan Quigley, David Stavens, Adam Coates, and Sebastian Thrun
Abstract:
The interpretation of uncertain sensor streams for localization is usually considered in the context of a robot. Increasingly, however, portable consumer electronic devices, such as smartphones, are equipped with sensors including WiFi radios, cameras, and inertial measurement units (IMUs). Many tasks typically associated with robots, such as localization, would be valuable to perform on such devices. In this paper, we present an approach for indoor localization exclusively using the low-cost sensors typically found on smartphones. Environment modification is not needed. We rigorously evaluate our method using ground truth acquired using a laser range scanner. Our evaluation includes overall accuracy and a comparison of the contribution of individual sensors. We find experimentally that fusion of multiple sensor modalities is necessary for optimal performance and demonstrate sub-meter localization accuracy.
Link:
IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), October 2010
http://www-cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.pdf
or
local_copy
Video:
http://www.cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.mp4
Authors: Morgan Quigley, David Stavens, Adam Coates, and Sebastian Thrun
Abstract:
The interpretation of uncertain sensor streams for localization is usually considered in the context of a robot. Increasingly, however, portable consumer electronic devices, such as smartphones, are equipped with sensors including WiFi radios, cameras, and inertial measurement units (IMUs). Many tasks typically associated with robots, such as localization, would be valuable to perform on such devices. In this paper, we present an approach for indoor localization exclusively using the low-cost sensors typically found on smartphones. Environment modification is not needed. We rigorously evaluate our method using ground truth acquired using a laser range scanner. Our evaluation includes overall accuracy and a comparison of the contribution of individual sensors. We find experimentally that fusion of multiple sensor modalities is necessary for optimal performance and demonstrate sub-meter localization accuracy.
Link:
IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), October 2010
http://www-cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.pdf
or
local_copy
Video:
http://www.cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.mp4
Monday, November 22, 2010
Lab Meeting November 22, 2010 (Andi): Three-Dimensional Mapping with Time-of-Flight Cameras
Title: Three-Dimensional Mapping with Time-of-Flight Cameras
Authors: Stefan May, David Droeschel, Dirk Holz, Stefan Fuchs, Ezio Malis, Andreas Nuechter and Joachim Hertzberg
Journal of Field Robotics 2009
Abstract: This article investigates the use of time-of-flight (ToF) cameras in mapping tasks for autonomous mobile robots, in particular in simultaneous localization and mapping (SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for threedimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. The paper first summarizes calibration and filtering approaches for improving the accuracy, precision, and robustness of ToF cameras independent of their intended usage. Then, several ego motion estimation approaches are applied or adapted, respectively, in order to provide a performance benchmark for registering ToF camera data. As a part of this, an extension to the iterative closest point algorithm has been developed that increases the robustness under restricted field of view and under larger displacements. Using an indoor environment, the paper provides results from SLAM experiments using these approaches in comparison. It turns out that the application of ToF cameras is feasible to SLAM tasks, although this type of sensor has a complex error characteristic.
Sunday, November 21, 2010
Lab Meeting November 22, 2010 (Alan): Temporary Maps for Robust Localization in Semi-static Environments (IROS 2010)
Title: Temporary Maps for Robust Localization in Semi-static Environments (IROS 2010)
Authors: Daniel Meyer-Delius, Jurgen Hess, Giorgio Grisetti, Wolfram Burgard
Abstract—Accurate and robust localization is essential for the successful navigation of autonomous mobile robots. The majority of existing localization approaches, however, is based on the assumption that the environment is static which does not hold for most practical application domains. In this paper, we present a localization framework that can robustly track a robot’s pose even in non-static environments. Our approach keeps track of the observations caused by unexpected objects in the environment using temporary local maps. It relies both on these temporary local maps and on a reference map of the environment for estimating the pose of the robot. Experimental results demonstrate that by exploiting the observations caused by unexpected objects our approach outperforms standard localization methods for static environments.
Link: pdf
Monday, November 15, 2010
Lab Meeting November 15( KuenHan ), 3D Reconstruction of a Moving Point from a Series of 2D Projections (ECCV 2010)
Title :3D Reconstruction of a Moving Point from a Series of 2D Projections
Author: Hyun Soo Park, Takaaki Shiratori, Iain Matthews, and Yaser Sheikh
Abstract
Author: Hyun Soo Park, Takaaki Shiratori, Iain Matthews, and Yaser Sheikh
Abstract
This paper presents a linear solution for reconstructing the 3D trajectory of a moving point from its correspondence in a collection of 2D perspective images, given the 3D spatial pose and time of capture of the cameras that produced each image. Triangulation-based solutions do not apply, as multiple views of the point may not exist at each instant in time. A geometric analysis of the problem is presented and a criterion, called reconstructibility, is defined to precisely characterize the cases when reconstruction is possible, and how accurate it can be. We apply the linear reconstruction algorithm to reconstruct the time evolving 3D structure of several real-world scenes, given a collection of non-coincidental 2D images.
LinkSunday, November 14, 2010
Lab Meeting November 15, 2010 (fish60): Unfreezing the Robot: Navigation in Dense, Interacting Crowds
Title: Unfreezing the Robot: Navigation in Dense, Interacting Crowds(IROS 2010)
Author: Peter Trautman and Andreas Krause
Abstract—In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary aneuvers) to avoid collisions. ... In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on Dependent Output Gaussian Processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference.
Link
Author: Peter Trautman and Andreas Krause
Abstract—In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary aneuvers) to avoid collisions. ... In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on Dependent Output Gaussian Processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference.
Link
Monday, November 01, 2010
CMU PhD Thesis Defense: Geolocation with Range: Robustness, Efficiency and Scalability
CMU RI PhD Thesis Defense
Joseph A. Djugash
Geolocation with Range: Robustness, Efficiency and Scalability
November 05, 2010, 10:00 a.m., NSH 1507
Abstract
[LINK][PDF]
Thesis Committee
Sanjiv Singh, Chair
George Kantor
Howie Choset
Wolfram Burgard, University of Freiburg
Joseph A. Djugash
Geolocation with Range: Robustness, Efficiency and Scalability
November 05, 2010, 10:00 a.m., NSH 1507
Abstract
This thesis explores the topic of geolocation with range. A robust method for localization and SLAM (Simultaneous Localization and Mapping) is proposed. This method uses a polar parameterization of the state to achieve accurate estimates of the nonlinear and multi-modal distributions in range-only systems. Several experimental evaluations on real robots reveal the reliability of this method.
Scaling such a system to large network of nodes, increases the computational load on the system due to the increased state vector. To alleviate this problem, we propose the use of a distributed estimation algorithm based on the belief propagation framework. This method distributes the estimation task, such that each node only estimates its local network, greatly reducing the computation performed by any individual node. However, the method does not provide any guarantees on the convergence of its solution in general graphs. Convergence is only guaranteed for non-cyclic graphs (ie. trees). Thus, an extension of this approach which reduces any arbitrary graph to a spanning tree is presented. This enables the proposed decentralized localization method to provide guarantees on its convergence.
[LINK][PDF]
Thesis Committee
Sanjiv Singh, Chair
George Kantor
Howie Choset
Wolfram Burgard, University of Freiburg
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