Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking

Author

Baig, Qadeer ; Perrollaz, Mathias ; Nascimento, J.B.D. ; Laugier, C.

Author_Institution

E-Motion team, Inria Rhone-Alpes, Montbonnot Saint Martin, France

fYear

2012

Firstpage

656

Lastpage

661

Abstract

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.

Keywords

Bayes methods; distance measurement; filtering theory; object tracking; optical radar; pattern clustering; signal classification; traffic engineering computing; BOF; Bayesian occupancy filter; FCTA; IMU information; SLAM; data grid; dynamic environment; fast classification; fast clustering-tracking algorithm; fast motion detection technique; laser data; lidar; occupancy information; odometry; simultaneous localization and mapping; static environment; tracking module; Bayes methods; Clustering algorithms; Motion detection; Radiation detectors; Tracking; Vehicles; BOF; FCTA; Motion Detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4673-1871-6

Electronic_ISBN

978-1-4673-1870-9

Type

conf

DOI

10.1109/ICARCV.2012.6485235

Filename

6485235