X-band radar based SLAM in Singapore's off-shore environment
Author(s)Hover, Franz S.; Patrikalakis, Nicholas M.; Mullane, John Stephen; Keller, Samuel; Rao, Akshay; Adams, Martin; Yeo, Anthony; ... Show more Show less
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This paper presents a simultaneous localisation and mapping (SLAM) algorithm implemented on an autonomous sea kayak with a commercial off-the-shelf X-band marine radar mounted. The Autonomous Surface Craft (ASC) was driven in an off-shore test site in Singapore's southern Selat Puah marine environment. Data from the radar, GPS and an inexpensive single-axis gyro data were logged by an on-board processing unit as the ASC traversed the environment, which comprised geographical and surface vessel landmarks. An automated feature extraction routine is presented, based on a probabilistic landmark detector, followed by a clustering and centroid approximation approach. With restrictive feature modeling, and a lack of vehicle control input information, it is demonstrated that via the novel RB-PHD-SLAM Filter, useful results can be obtained, despite an actively rolling and pitching ASC on the sea surface. In addition, the merits of investigating ASC SLAM are demonstrated, particularly with respect to the map estimation, obstacle avoidance and target tracking problems. Despite the presence of GPS and gyro data, heading information on such small ASC's is greatly compromised which induces large sensing error, further accentuate by the large range of the radar sensor. This work is a step towards realising an ASC capable of performing environmental or security surveillance and reporting a real-time active awareness of the above-water scene.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Proceedings of the 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV)
Institute of Electrical and Electronics Engineers (IEEE)
Mullane, John et al. X-band radar based SLAM in Singapore's off-shore environment.” Proceedings of the 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010. 398–403.
Final published version