NDLI: Likelihood-field-model-based vehicle tracking with Velodyne

Content Provider	IEEE Xplore Digital Library
Author	Chen, Tongtong Dai, Bin Liu, Daxue Song, Jinze
Copyright Year	2015
Description	Author affiliation: College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, China (Chen, Tongtong; Dai, Bin; Liu, Daxue) \|\| Graduate School National University of Defense Technology, Changsha, China (Song, Jinze)
Abstract	Vehicle tracking is an important module for an Autonomous Land Vehicle (ALV) navigation in urban environments. In this paper, we propose a novel vehicle tracking module with a Bayesian filter based on the likelihood field model for our ALV, which is equipped with a Velodyne LIDAR and an Inertial Navigation System (INS). At each time step t, Scaling Series importance sampling algorithm is ran on the associated measurements of each tracker with an uniform prior to obtain a weighted particle set. Each particle represents a possible prior pose of the tracker in the current scan. Then for each particle, its weight is adjusted with the weighted particles at time step t − 1 via Bayesian recursion equation to capture the vehicle dynamic model. These final weighted particles approximate the posterior belief of the corresponding tracker and the one with the maximum weight is chosen as the output of the tracking result at time step t. Both the quantitative and qualitative performance of our vehicle tracking algorithm is validated on the Velodyne data collected by our ALV in various environments.
Starting Page	1566
Ending Page	1571
File Size	1039813
Page Count	6
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7408134
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicle tracking Atmospheric measurements Heuristic algorithms Computational modeling Particle measurements Bayes methods ALV Vehicle dynamics Likelihood-field model Vehicles
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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