  A novel probabilistic perception algorithm is presented as a real-time joint
solution to data association, object tracking, and object classification for an
autonomous ground vehicle in all-weather conditions. The presented algorithm
extends a Rao-Blackwellized Particle Filter originally built with a particle
filter for data association and a Kalman filter for multi-object tracking
(Miller et al. 2011a) to now also include multiple model tracking for
classification. Additionally a state-of-the-art vision detection algorithm that
includes heading information for autonomous ground vehicle (AGV) applications
was implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded and
used to experimentally examine if and how state-of-the-art vision algorithms
can complement or replace lidar and radar sensors. Sensor and algorithm
performance in adverse weather and lighting conditions is tested. Experimental
evaluation demonstrates robust all-weather data association, tracking, and
classification where camera, lidar, and radar sensors complement each other
inside the joint probabilistic perception algorithm.

Systems and Control;Computer Vision and Pattern Recognition;Machine Learning;Robotics