An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

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.

A data association model for analysis of crowd structure

“…where BB in is the bounding box of the n-th person and BB gm is the outline of the m-th group. Alternatively, objects (including individuals and groups) can be represented by density functions (e.g., Li et al, 2018) or by heat maps (e.g., Zhou et al, 2019) where object outlines are defined by (near-)zero values of these functions. Then Eqn.…”