Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 18, Issue 7, Pages 1824-1834Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2016.2616718
Keywords
Motion estimation; object tracking; artificial intelligence; autonomous vehicles; sensor fusion; computer vision
Categories
Funding
- Project STIRPE [DPI2013-46897-C2-1-R]
- Agencia Canaria de Investigacion, Innovacion y Sociedad de la informacion
- FEDER Funds (EU)
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In this paper, a new method for real-time detection, motion estimation, and tracking of generic obstacles using just a 3-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-based 3-D object segmentation and a motion estimation scheme. That combination of techniques leverages 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 input point cloud. Another one compares the tracking and detection results with other state-of-the-art methods. Last tests are intended for the characterization of the execution times required. Results are encouraging, with a high detection rate, low error rate, and real-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-detection and ranging generated point clouds as an input.
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