大規模動態環境下的機器人定位

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 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. 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.