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Paper details
Number 3 - September 2018
Volume 28 - 2018
An ant-based filtering random-finite-set approach to simultaneous localization and mapping
Demeng Li, Jihong Zhu, Benlian Xu, Mingli Lu, Mingyue Li
Abstract
Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation
in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a
feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows
decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the
vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an
ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful
search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process.
Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory.
Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency
with the results obtained from numerical simulations.
Keywords
simultaneous localization and mapping (SLAM), random finite sets, probability hypothesis density, ant colony