This paper describes the design and experimental evaluation of a system that enables a vehicle to detect and track moving objects in real-time. The approach investigated in this work detects objects in LADAR scan lines and tracks these objects (people or vehicles) over time. The system can fuse data from multiple scanners for 360 deg. coverage. The resulting tracks are then used to predict the most likely future trajectories of the detected objects. The predictions are intended to be used by a planner for dynamic object avoidance. The perceptual capabilities of our system form the basis for safe and robust navigation in robotic vehicles, necessary to safeguard soldiers and civilians operating in the vicinity of the robot.
Predictive Mover Detection and Tracking in Cluttered Environments
2006
9 pages
Report
No indication
English
Computers, Control & Information Theory , Infrared & Ultraviolet Detection , Navigation Systems , Detection , Robots , Tracking , Collision avoidance , Optical radar , Multisensors , Data fusion , Navigation , Kalman filtering , Symposia , Component reports , Object detection , Object tracking , Autonomous vehicles , Detection distance , Ladar , Moving objects , Obstacle avoidance , Robotic vehicles , Track breakup , Trajectory prediction , Velocity delay
PCRLB for Tracking in Cluttered Environments: Measurement Sequence Conditioning Approach
Online Contents | 2006
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