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A novel probabilistic perception algorithm is presented as a real-time jointsolution to data association, object tracking, and object classification for anautonomous ground vehicle in all-weather conditions. The presented algorithmextends a Rao-Blackwellized Particle Filter originally built with a particlefilter for data association and a Kalman filter for multi-object tracking(Miller et al. 2011a) to now also include multiple model tracking forclassification. Additionally a state-of-the-art vision detection algorithm thatincludes heading information for autonomous ground vehicle (AGV) applicationswas implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded andused to experimentally examine if and how state-of-the-art vision algorithmscan complement or replace lidar and radar sensors. Sensor and algorithmperformance in adverse weather and lighting conditions is tested. Experimentalevaluation demonstrates robust all-weather data association, tracking, andclassification where camera, lidar, and radar sensors complement each otherinside the joint probabilistic perception algorithm.

All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles