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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