Lab Meeting August 31st, 2010 (David): Scene Understanding in a Large Dynamic Environment through a Laser-based Sensing (ICRA'10)

Title:

Scene Understanding in a Large Dynamic Environment through a Laser-based Sensing

Authors:

Huijing Zhao, Yiming Liu, Xiaolong Zhu, Yipu Zhao, Hongbin Zha

Abstract:

It became a well known technology that a map of complex environment containing low-level geometric primitives (such as laser points) can be generated using a robot with laser scanners. This research is motivated by the need of obtaining semantic knowledge of a large urban outdoor environment after the robot explores and generates a low-level sensing data set. An algorithm is developed with the data represented in a range image, while each pixel can be converted into a 3D coordinate. Using an existing segmentation method that models only geometric homogeneities, the data of a single object of complex geometry, such as people, cars, trees etc., is partitioned into different segments. Such a segmentation result will greatly restrict the capability of object recognition. This research proposes a framework of simultaneous segmentation and classification of range image, where the classification of each segment is conducted based on its geometric properties, and homogeneity of each segment is evaluated conditioned on each object class. Experiments are presented using the data of a large dynamic urban outdoor environment, and performance of the algorithm is evaluated.

local copy : [link]

[link]

Lab Meeting 10/28 (Any): GroupSAC

Kai Ni, Hailin Jin, and Frank Dellaert. GroupSAC: Efficient Consensus in the Presence of Groupings. In International Conference on Computer Vision (ICCV), September 2009.

Abstract--We present a novel variant of the RANSAC algorithmthat is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based on which we introduce a new binomial mixture model rather than the simple binomial model as used in RANSAC. We prove that in the new model it is more efficient to sample data from a smaller numbers of groups and groups with more tentative correspondences, which leads to a new sampling procedure that uses progressive numbers of groups. We demonstrate our algorithm on two classical geometric vision problems: wide-baseline matching and camera resectioning. The experiments show that the algorithm servesas a general framework that works well with three possible grouping strategies investigated in this paper, including a novel optical flow based clustering approach. The results show that our algorithm is able to achieve a significant performance gain compared to the standard RANSAC and PROSAC.

Lab Meeting August 17, 2009 (Any): RANSAC-based DARCES

RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 21, No. 11, November 1999

Chu-Song Chen, Yi-Ping Hung, and Jen-Bo Cheng

Abstract: In this paper, we propose a new method, the RANSAC-based DARCES method, which can solve the partially overlapping 3D registration problem without any initial estimation. For the noiseless case, the basic algorithm of our method can guarantee that the solution it finds is the true one, and its time complexity can be shown to be relatively low. An extra characteristic is that our method can be used even for the case that there are no local features in the 3D data sets.

Link

Lab Meeting June 8th, 2009 (Any): CRF-Filters

Paper title: CRF-Filters: Discriminative Particle Filters for Sequential State Estimation

Authors: Benson Limketkai, Dieter Fox and Lin Liao

Appears in: ICRA 2007

Abstract: Particle filters have been applied with great success to various state estimation problems in robotics. However, particle filters often require extensive parameter tweaking in order to work well in practice. This is based on two observations. First, particle filters typically rely on independence assumptions such as “the beams in a laser scan are independent given the robot’s location in a map”. Second, even when the noise parameters of the dynamical system are perfectly known, the sample-based approximation can result in poor filter performance. In this paper we introduce CRF-Filters, a novel variant of particle filtering for sequential state estimation. CRF-Filters are based on conditional random fields, which are discriminative models that can handle arbitrary dependencies between observations. We show how to learn the parameters of CRF-Filters based on labeled training data. Experiments using a robot equipped with a laser range-finder demonstrate that our technique is able to learn parameters of the robot’s motion and sensor models that result in good localization performance, without the need of additional parameter tweaking.

Full text: PDF

Lab Meeting April 6, 2009 (Any): RANSAC: An Historical Perspective

In past few years, robust statistical methods have significantly contributed to the advances made in computer vision. In particular, the class of random-sample consensus, or RANSAC type algorithms for the solution of problems in robust estimation has been key to progress.

The fitting problem is approached in the opposite way from most previous techniques, such as the least squares approaches. Instead of averaging all the measurements and then trying to throw out bad ones, the smallest number of measurements is used to compute a model’s unknown parameters and then evaluated the instantiated model by counting the number of consistent samples.

I will review the RANSAC technique and some of its variants/applications, as well as the future research directions.

CVPR 2006 Workshop: 25 Years of RANSAC - http://cmp.felk.cvut.cz/ransac-cvpr2006/

Lab Meeting January 19, 2009 (Yu-chun): Interaction with a Zoomorphic Robot that Exhibits Canid Mechanisms of Behaviour

ICRA 2008

Title: Interaction with a Zoomorphic Robot that Exhibits Canid Mechanisms of Behaviour

Authors: Trevor Jones1, Shaun Lawson, and Daniel Mills

Abstract:

Despite parallels between the cooperative use of domestic dogs in human society today, the predicted similar deployment of robots in the future, and the plethora of superficially dog-like robotic entertainment devices, very little effort has been directed at exploiting any understanding of social cognition between dogs and humans when designing interactive robotic systems. This paper describes an experiment in which we gave interactive robots zoomorphic appearances and dog-like behavioural properties. We analysed human reactions to robots exhibiting differing levels of zoomorphism and dog-like behaviour during an interaction task; we were particularly interested to determine whether behaviour and/or appearance that mimicked that of dogs facilitated increased satisfaction in robot performance and a willingness to persevere with a robot that made mistakes. Our findings show that neither the appearance or behaviour of a robot had an impact on the participants’ rating of robot performance whilst there was also no significant difference in the self-reported categories of frustration, excitement and desire to persist with an interaction. However, our findings suggest that differences in individual preferences are revealed when people are asked to interact with robots that exhibit dog-like behaviours and other zoomorphic characteristics and that further research is required in order to better understand these differences.

link

Lab Meeting November 10, 2008 (Any): Efficiently Learning High-dimensional Observation Models for Monte-Carlo Localization using Gaussian Mixtures

Title: Efficiently Learning High-dimensional Observation Models for Monte-Carlo Localization using Gaussian Mixtures

Authors: Patrick Pfaff, Cyrill Stachniss, Christian Plagemann, and Wolfram Burgard

Abstract: Whereas probabilistic approaches are a powerful tool for mobile robot localization, they heavily rely on the proper definition of the so-called observation model which defines the likelihood of an observation given the position and orientation of the robot and the map of the environment. Most of the sensor models for range sensors proposed in the past either consider the individual beam measurements independently or apply uni-modal models to represent the likelihood function. In this paper, we present an approach that learns place-dependent sensor models for entire range scans using Gaussian mixture models. To deal with the high dimensionality of the measurement space, we utilize principle component analysis for dimensionality reduction. In practical experiments carried out with data obtained from a real robot, we demonstrate that our model substantially outperforms existing and popular sensor models.

Local copy

Lab Meeting September 29, 2008 (Any): SCAPE: Shape Completion and Animation of People

D. Anguelov, P.Srinivasan, D.Koller, S.Thrun, J. Rodgers, J.Davis. SCAPE: Shape Completion and Animation of People. Proceedings of the SIGGRAPH Conference, 2005.

Abstract—We introduce the SCAPE method (Shape Completion and Animation for PEople)—a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion — generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.

Full text

SCAPE project page

Lab Meeting August 11, 2008 (Any): Model Based Vehicle Tracking for Autonomous Driving in Urban Environments

Title: Model Based Vehicle Tracking for Autonomous Driving in Urban Environments

Authors: Anna Petrovskaya and Sebastian Thrun

Abstract: Situational awareness is crucial for autonomous driving in urban environments. This paper describes moving vehicle tracking module that we developed for our successful entry in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter. We also show how to build efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles.

In contrast to prior art, we propose a model based approach which encompasses both geometric and dynamic properties of the tracked vehicle in a single Bayes filter. The approach naturally handles data segmentation and association, so that these pre-processing steps are not required.

RSS Online Proceedings: here
Abstract: here
PDF: here

Lab Meeting July 7th, 2008 (Any): Classifying Dynamic Objects: An Unsupervised Learning Approach

Title: Classifying Dynamic Objects: An Unsupervised Learning Approach
Authors: Matthias Luber, Kai O. Arras, Christian Plagemann, and Wolfram Burgard

Abstract: For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.

PDF via Robotics: Science and Systems IV

Lab Meeting June 9th, 2008 (Yu-chun): GUMSAWS: A Generic User Modeling Server for Adaptive Web Systems

Communication Networks and Services Research, 2007

Author: Jie Zhang and Ali A. Ghorbani

Abstract:
In this paper we focus on the architecture, design and implementation of a generic user modeling server for adaptive web systems (GUMSAWS), reaching the goals of generality, extendability and replaceability. GUMSAWS acts as a centralized user modeling server to assist several adaptive web systems (possibly in different domains) concurrently. It incrementally builds up user models, provides functions of storing, updating and deleting entries in user profiles, and maintains consistency of user models. Our system is also able to infer missing entries in user profiles from different information sources, including direct information, groups information, association rules and general facts. We further evaluate its inference performance within the context of e-commerce. Experimental results show that the average accuracy of inferring user missing property values from different information resources is found to be almost 70%. We also use a personalized electronic news system to demonstrate the example of our system in use.


link

Lab Meeting April 14th, 2008 (Any): Probabilistic Terrain Analysis For High-Speed Desert Driving

Abstract--The ability to perceive and analyze terrain is a key problem in mobile robot navigation. Terrain perception problems arise in planetary robotics, agriculture, mining, and, of course, self-driving cars. Here, we introduce the PTA (probabilistic terrain analysis) algorithm for terrain classication with a fastmoving robot platform. The PTA algorithm uses probabilistic techniques to integrate range measurements over time, and relies on efficient statistical tests for distinguishing drivable from nondrivable terrain. By using probabilistic techniques, PTA is able to accommodate severe errors in sensing, and identify obstacles with nearly 100% accuracy at speeds of up to 35mph. The PTA algorithm was an essential component in the DARPA Grand Challenge, where it enabled our robot Stanley to traverse the entire course in record time.

S. Thrun, M. Montemerlo, and A. Aron. Probabilistic terrain analysis for high-speed desert driving. In G. Sukhatme, S. Schaal, W. Burgard, and D. Fox, editors, Proceedings of the Robotics Science and Systems Conference, Philadelphia, PA, 2006.

Lab Meeting 13 November (Any): An Efficient FastSLAM Algorithm for Generating Maps of Large-Scale Cyclic Environments from Raw Laser Range Measurement

Dirk Hähnel, Wolfram Burgard, Dieter Fox and Sebastian Thrun

Intl. Conference on Intelligent Robots and Systems

The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems. This paper presents a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches. - Link.

Lab Meeting 15 October (Der-Yeuan): Introduction to Robotics Programming with Microsoft Robotics Studio

Abstract:

Microsoft Robotics Studio (MSRS) is a Windows-based IDE for robotics programming. Its primary components are the Concurrency and Coordination Runtime (CCR) and the Decentralized System Services (DSS). The CCR emphasizes in scheduling the tasks to manage concurrency and load-balancing for different applications. The DSS is a service-oriented approach to robot component integration where every software or hardware component of a design is a service. Such web-based architecture allows services within a network to interact. Given the experience of MSRS with LEGO NXT bricks, this presentation will provide a brief introduction to CCR and DSS, and give some insight on the maturity of MSRS.

Lab Meeting 8 October (Any): SLAM in Large-Scale Cyclic Environments Using the Atlas Framework

Michael Bosse, Paul Newman, John Leonard, Seth Teller

International Journal of Robotics Research 2004 (IJRR'04)

Abstract -- In this paper we describe Atlas, a hybrid metrical/topological approach to simultaneous localization and mapping (SLAM) that achieves efficient mapping of large-scale environments. The representation is a graph of coordinate frames, with each vertex in the graph representing a local frame and each edge representing the transformation between adjacent frames. In each frame, we build a map that captures the local environment and the current robot pose along with the uncertainties of each. Each map’s uncertainties are modeled with respect to its own frame. Probabilities of entities with respect to arbitrary frames are generated by following a path formed by the edges between adjacent frames, computed using either the Dijkstra shortest path algorithm or breath-first search. Loop closing is achieved via an efficient map-matching algorithm coupled with a cycle verification step.We demonstrate the performance of the technique for post-processing large data sets, including an indoor structured environment (2.2 km path length) with multiple nested loops using laser or ultrasonic ranging sensors.

Full Article - Link.
Video - Link.

Lab Meeting 27 August (Chihao): Demonstration of Acoustic Localization in PAL2

I will show the acoustic localization system in PAL2.
This system could find the direction of sound audio source even if the source is moving.

Lab Meeting 30 July (Any): Map-Based Precision Vehicle Localization in Urban Environments

Jesse Levinson, Michael Montemerlo, and Sebastian Thrun

Robotics: Science and Systems III

Abstract:
Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings.
We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution.
To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10-centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic.

Paper (PDF): Link

Lab Meeting 6 June (Any): A New Approach for Large-Scale Localization and Mapping: Hybrid Metric-Topological SLAM

Authors:
Jose-Luis Blanco, Juan-Antonio Fernández, Javier Gonzalez
Dept. of System Engineering and Automation
University of Malaga
Málaga, Spain

From:
ICRA'07

Abstract:

Most successful works in Simultaneous Localization and Mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metrictopological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental contributions are: (i) the estimation of the topological path, an improvement similar to that of Rao-Blackwellized Particle Filters (RBPF) and FastSLAM in the field of metric map building; and (ii) the application of grounded methods to the abstraction of topology (including loop closure) from raw sensor readings. It is remarkable that our approach could be still represented as a Bayesian inference problem, becoming an extension of purely metric SLAM. Besides providing the formal definitions and the basics for our approach, we also describe a practical implementation aimed to real-time operation. Promising experimental results mapping large environments with multiple nested loops (~30.000 m2, ~2Km robot path) validate our work.

Lab Meeting 17 May (Any): Robust Monte Carlo Localization for Mobile Robots

Authors: Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert
From: Artificial Intelligence 128 (2001) 99-141

Abstract:
Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot’s belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called Mixture- MCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.

Lab Meeting 1 Feb 2007 (Yu-Chun): Integrating the OCC Model of Emotions in Embodied Characters

Christoph Bartneck
Workshop on Virtual Conversational Characters: Applications, Methods, and Research Challenges, 2002
[Link]

Abstract:
The OCC (Ortony, Clore, & Collins, 1988) model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This paper points out what the OCC model is able to do for an embodied emotional character and what it does not. Missing features include a history function, a personality designer and the interaction of the emotional categories.