![]() First, we extend the particle filter to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). We take as our starting point the single-robot Rao-Blackwellized particle filter described in and make two key generalizations. ![]() ![]() Index Terms - SLAM, Rao-Blackwellized particle filter, adaptive resampling, motion-model, improved proposalĪbstract - This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). Experimental results carried out with real mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approaches. Furthermore, we present an approach to selectively carry out resampling operations which seriously reduces the problem of particle depletion. This drastically decreases the uncertainty about the robot’s pose in the prediction step of the filter. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. In this paper, we present adaptive techniques for reducing this number in a Rao-Blackwellized particle filter for learning grid maps. Accordingly, a key question is how to reduce the number of particles. ![]() This approach uses a particle filter in which each particle carries an individual map of the environment. Abstract - Recently, Rao-Blackwellized particle filters have been introduced as an effective means to solve the simultaneous localization and mapping problem. ![]()
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May 2023
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