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Abstract. With recent advances in real-time implementations of filters for solving the simultaneous localization and mapping (SLAM) problem in the range-sensing domain, attention has shifted to implementing SLAM solutions using vision-based sensing. This paper presents and analyses different models of the Rao-Blackwellised particle filter (RBPF) for vision-based SLAM within a comprehensive application architecture. The main contributions of our work are the introduction of a new robot motion model utilizing structure from motion (SFM) methods and a novel mixture proposal distribution that combines local and global pose estimation. In addition, we compare these under a wide variety of operating modalities, including monocular sensing and the standard odometry-based methods. We also present a detailed study of the RBPF for SLAM, addressing issues in achieving real-time, robust and numerically reliable filter behavior. Finally, we present experimental results illustrating the improved accuracy of our proposed models and the efficiency and scalability of our implementation.

Older adults with cognitive impairments are generally prohibited from using powered wheelchairs, because of the high risk of collisions with people and objects. This paper describes and presents the preliminary results of a system that uses an infrared sensor to provide anticollision and a prompting system for a powered wheelchair that helps guide the user safely past obstacles. Trials with the prototyped system detected collisions and stopped the chair in 95% of trials with an object and generated no false alarms.

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