A Combined Voxel and Particle Filter Filter-Based Based Approach for Fast Obstacle Detection and Tracking in Automotive Applications
Abstract: In this paper, a new method for real real-time time detection, motion estimation, and tracking of generic obstacles using just a 33-D point cloud and odometry information as input is presented. In this approach, a simplification of the world is done using voxels, supported by a particle filter filter-based 3-D D object segmentation and a motion estimation scheme. That combination of techniques leverages le a fast and reliable object detection, providing also motion speed and direction information. Four detailed studies have been performed in order to assess the suitability of the method, two of them related to the parameterization of the method and its ts input point cloud. Another one compares the tracking and detection results with other state state-of-the-art art methods. Last tests are intended for the characterization of the execution times required. Results are encouraging, with a high detection rate, low er error rate, and real-time time capable computing performance. In the attached video, it is possible to observe the behavior of the method, both using a stereovision and a light light-detection detection and ranging generated point clouds as an input.