NTU PAL Thesis Defense: Mobile Robot Localization in Large-scale Dynamic Environments

Mobile Robot Localization in Large-scale Dynamic Environments

Shao-Wen Yang

Doctoral Dissertation Defense

Department of Computer Science and Information Engineering

National Taiwan University

Time: Thursday, 19 May, 2011 at 8:00AM +0800 (CST)

Location: R542, Der-Tian Hall

Advisor: Chieh-Chih Wang

Thesis Committee:

Li-Chen Fu

Jane Yung-Jen Hsu

Han-Pang Huang

Ta-Te Lin

Chu-Song Chen, Sinica

Jwu-Sheng Hu, NCTU

John J. Leonard, MIT

Abstract:

Localization is the most fundamental problem to providing a mobile robot with autonomous capabilities. Whilst simultaneous localization and mapping (SLAM) and moving object tracking (MOT) have attracted immense attention in the last decade, the focus of robotics continues to shift from stationary robots in a factory automation environment to mobile robots operating in human-inhabited environments. State of the art relying on the static world assumption can fail in the real environment that is typically dynamic. Specifically, the real environment is challenging for mobile robots due to the variety of perceptual inconsistency over space and time. Development of situational awareness is particularly important so that the mobile robots can adapt quickly to changes in the environment.

In this thesis, we explore the problem of mobile robot localization in the real world in theory and practice, and show that localization can benefit from both stationary and dynamic entities.

The performance of ego-motion estimation depends on the consistency between sensory information at successive time steps, whereas the performance of localization relies on the consistency between the sensory information and the a priori map. The inconsistencies make a robot unable to robustly determine its location in the environment. We show that mobile robot localization, as well as ego-motion estimation, and moving object detection are mutually beneficial. Most importantly, addressing the inconsistencies serves as the basis for mobile robot localization, and forms a solid bridge between SLAM and MOT.

Localization, as well as moving object detection, is not only challenging but also difficult to evaluate quantitatively due to the lack of a realistic ground truth. As the key competencies for mobile robotic systems are localization and semantic context interpretation, an annotated data set, as well as an interactive annotation tool, is released to facilitate the development, evaluation and comparison of algorithms for localization, mapping, moving object detection, moving object tracking, etc.

In summary, a unified stochastic framework is introduced to solve the problems of motion estimation and motion segmentation simultaneously in highly dynamic environments in real time. A dual-model localization framework that uses information from both the static scene and dynamic entities is proposed to improve the localization performance by explicitly incorporating, rather than filtering out, moving object information. In the ample experiment, a sub-meter accuracy is achieved, without the aid of GPS, which is adequate for autonomous navigation in crowded urban scenes. The empirical results suggest that the performance of localization can be improved when handling the changing environment explicitly.

Download:

• Thesis draft: http://any.csie.ntu.edu.tw/thesis/yang_thesis-v1_0.pdf

CMU PhD Thesis Proposal: Robust Monocular Vision-based Navigation for a Miniature Fixed-Wing Aircraft

PhD Thesis Proposal
Myung Hwangbo
Carnegie Mellon University

September 15, 2009, 1:00 p.m., Newell Simon Hall 1109

Title:
Robust Monocular Vision-based Navigation for a Miniature Fixed-Wing Aircraft

Abstract:
Recently the operation of unmanned aerial vehicles (UAVs) has expanded from military to civilian applications. Contrary to remote-controlled tasks in a high altitude, low-altitude flight in an urban environment requires a higher level of autonomy to respond to complex and unpredictable situations. Vision-based methods for autonomous navigation have been a promising approach because of multi-layered information delivered by images but their robustness in various situations has been hard to achieve. We propose a series of monocular computer vision algorithms combined with vehicle dynamics and other navigational sensors in GPS-denied environments like an urban canyon. We use a fixed-wing model airplane of 1m wing span as our UAV platform. Because of its small payload and limited communication bandwidth to off-body processors, particular attention is paid to both realtime and robustness at every level of vision processing of low-grade images.

In point-to-point navigation, state estimation is based on the structure-from-motion method (SFM) using natural landmarks under conditions where the captured images have sufficient texture. To cope with the fundamental limits of monocular visual odometry (scale ambiguity and rotation-translation ambiguity), vehicle dynamics and airspeed measurements are incorporated in a Kalman filter framework. More robust estimation is provided from multiple rails of the SFM which are traced in an interweaving fashion. Sturdy input to the SFM is enabled by optical flow computation which is tightly coupled with the IMU. Predictive warping parameters and a high-order motion model enhance the accuracy and life span of KLT feature tracking. We also employ vision-based horizon detection as an absolute attitude sensor which is useful for low-level control of a UAV.

The performance of the proposed method is evaluated in what we call an air-slalom task, where the UAV is expected to pass through multiple gates in the air in a row. It will demonstrate how a fixed-wing UAV confronts its limited agility, which is inferior to other hovercraft types in typical urban operations. To efficiently find a feasible obstacle-free path to a goal, we propose a 3D Dubins heuristic for optimal cost to a goal and use a set of lateral and longitudinal motion primitives interconnecting at trim states in order to reduce the dimension of configuration space. We first demonstrate our visual navigation in our UAV simulator, which can be switched between live and synthetic modes, each including wireless data transmission to a ground station.
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Thesis committee:
Takeo Kanade, Co-chair 
James Kuffner, Co-chair 
Sanjiv Singh 
Omead Amidi 
Randy Beard, Brigham Young University

Robot PAL PhD Thesis Proposal: Towards Robust Localization in Highly Dynamic Environments

Shao-Wen Yang

Proposal for Doctoral Thesis

Thesis Committee:

Chieh-Chih Wang (Chair)

Li-Chen Fu

Jane Yung-Jen Hsu

Han-Pang Huang

Ta-Te Lin

John J. Leonard, MIT

Date: January 12 2009

Time: 1:00pm

Place: R524

Abstract--Localization in urban environments is a key prerequisite for making a robot truly autonomous, as well as an important issue in collective and cooperative robotics. It is not easily achievable when moving objects are involved or environment changes. Ego-motion estimation is the problem of determining the pose of a robot relative to its previous location without an absolute frame of reference. Mobile robot localization is the problem of determining the pose of a robot relative to a given map of the environment. The performance of ego-motion estimation completely depends on the consistency between sensor information at successive time steps, whereas the performance of global localization highly depends on the consistency between the sensor information and the a priori environment knowledge. The inconsistencies make a robot unable to robustly localize itself in real environments. Explicitly taking into account the inconsistencies serves as the basis for mobile robot localization.

In this thesis, we explore the problem of mobile robot localization in highly dynamic environments. We proposed a multiple-model approach to solve the problems of ego-motion estimation and moving object detection jointly in a random sample consensus (RANSAC) paradigm. We show that accurate identification of static environments can help classification of moving objects, whereas discrimination of moving objects also yields better ego-motion estimation, particularly in environments containing a significant percentage of moving objects.

It is believed that a solution to the moving object detection problem can provide a bridge between the simultaneous localization and mapping (SLAM) and the detection and tracking of moving objects (DATMO) problems. Based on the ego-motion estimation framework, to provide reliable moving object detection, data association can still be problematic due to merge and split of objects and temporal occlusion. We propose the use of discriminative models to reason about the joint association between measurements. Scaling such a system to solve the global localization problem will increase the reliability for mobile robots to perform autonomous tasks in crowded urban scenes. We propose to use a multiple-model approach based on the probabilistic mobile robot localization framework and formulate an extension to the global localization problem. Besides, detecting objects of small sizes at low speeds, such as pedestrians, is difficult, but of particular interest in mobile robotics. We propose the use of prior knowledge from the mobile robot localization framework to deal with the problem of pedestrian detection, and formalize the localization-by-detection and detection-by-localization framework. The proposed approach will be demonstrated using experimental testing with real data.

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