Multi-Robot Simultaneous Localization and Mapping with Generalized Objects – Tackling Unstable Communication and Tracking Maneuver Switching Objects
Date Issued
2015
Date
2015
Author(s)
Chang, Chun-Hua
Abstract
To facilitate simultaneous localization and mapping (SLAM) in dynamic environments, the theoretical framework of simultaneous localization, mapping and moving object tracking (SLAMMOT) has been proposed, in which two solutions exist: SLAM with detection and tracking of moving objects (SLAM with DATMO) and SLAM with generalized objects (SLAM with GO). In SLAM with DATMO, measurements of moving objects are separated from those of static objects and are not utilized in the SLAM estimator. In SLAM with GO, the states of the robot, static objects and moving objects are jointly estimated, and thus SLAM and moving object tracking (MOT) can contribute to each other. The main goal of this thesis is to extend SLAM with GO to multi-robot scenarios and demonstrate the effectiveness of incorporating moving objects in practical domains. The multi-robot simultaneous localization and tracking (MR-SLAT) algorithm is proposed and evaluated in two scenarios, RoboCup and traffic. It will be demonstrated that by incorporating moving objects, MR-SLAT outperforms single robot localization and cooperative localization, and incorporating moving objects serves as the key to several localization challenges. Furthermore, for tackling practical unstable communication issues, a hybrid algorithm, communication adaptive MR-SLAT (ComAd MR-SLAT), is proposed to combine the advantages of two existing information sharing strategies, measurement-sharing and belief-sharing. In ComAd MR-SLAT, the sharing strategy is adaptively switched online according to the communication condition in order to optimize the estimation performance. The effectiveness of incorporating moving objects is also demonstrated in the challenging monocular scenario. It will be pointed out that incorporating moving objects with uncertain maneuvers suffers from the risk to downgrade the overall SLAM with GO performance. Accordingly, a maneuver switching detection (MSD) algorithm is proposed to extend monocular SLAM with GO to practical scenes in which the maneuvers of objects can switch. To integrate the multiple model (MM) approach from the tracking literature, a Gibbs sampling based MSD algorithm is designed to tackle the exponential complexity issue. Through ample experiments in this thesis, the practical applicability of the proposed algorithms and the effectiveness of incorporating moving objects are demonstrated.
Subjects
Localization
SLAM
Tracking
Multi-Robot
Communication
RoboCup
Intelligent Vehicle
Type
thesis
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