Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking
Qadeer Baig, Mathias Perrollaz, Jander Botelho do Nascimento, Christian Laugier · 2012
In this paper we present some important improvements to a fast motion detection technique based on laser data and odometry/imu information. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between two consecutive data grids. Then we show its integration with Bayesian Occupancy Filter (BOF) and with the subsequent tracking module called Fast Clustering-Tracking Algorithm (FCTA). We especially show the improvements achieved in tracking results after this integration.