Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/35697
Title: A combined voxel and particle filter-based approach for fast obstacle detection and tracking in automotive applications
Authors: Morales, Nestor
Toledo, Jonay
Acosta, Leopoldo
Sanchez-Medina, Javier 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Motion estimation
Object tracking
Artificial intelligence
Autonomous vehicles
Sensor fusion, et al
Issue Date: 2017
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: 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.
URI: http://hdl.handle.net/10553/35697
ISSN: 1524-9050
DOI: 10.1109/TITS.2016.2616718
Source: IEEE Transactions on Intelligent Transportation Systems[ISSN 1524-9050],v. 18 (7725942), p. 1824-1834
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