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Appearance-Based Mapping using Minimalistic Sensor Models
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BibTeX
@MISC{Rybski_appearance-basedmapping,
author = {Paul E. Rybski and Stergios Roumeliotis and Maria Gini and Nikolaos Papanikopoulos},
title = {Appearance-Based Mapping using Minimalistic Sensor Models},
year = {}
}
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Abstract
This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps as they are less sensitive to poor odometry estimates and position errors. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor “signature”, based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. We propose a method for incrementally building topological maps for a robot which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features. Two algorithms are implemented to address this problem. The Iterated form of the Extended Kalman Filter (IEKF) and a batch-processed linearized ML estimator are compared under various odometric noise models. 1
Keyphrases
appearance-based mapping minimalistic sensor model topological map implicit position measurement iterated form standard slam algorithm mobile robot distinctive feature position error metric distance robot position non-linear estimation problem map construction poor odometry estimate panoramic camera various location distinctive sensor signature high-fidelity geometric map image recognition extended kalman filter various odometric noise model indoor environment batch-processed linearized ml estimator current location
