Simultaneous localization and mapping with moving object tracking in 3D range data

Peng Mun Siew, Richard Linares, Vibhor L. Bageshwar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A Bayesian framework is designed for simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) using only 3D range data. Bayesian formulated occupancy grid maps are used to store and represent the occupancy probability of the environment. Two separate maps (static occupancy grid map and dynamic occupancy grid map) are generated and updated at each instance. The static occupancy grid map functions as the global map and is used to localized the platform using iterative closest point, whereas the dynamic occupancy grid map contains all the information of possible dynamic objects which are used by the Probability Hypothesis Density (PHD) filter for multiple target tracking. The robustness of the PHD filter is leveraged to enable the usage of a more aggressive dynamic voxel detection algorithm when constructing the dynamic occupancy grid map. Data augmentation is introduced to compensate for “infinity return” to further improve the framework’s robustness. The proposed framework was tested on mid-end HDL-32E and high-end HDL-64E LiDAR data obtained from Velodyne LiDAR and KITTI Dataset respectively, and has shown promising results for both cases.

Original languageEnglish (US)
Title of host publicationAIAA Information Systems-AIAA Infotech at Aerospace
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition209989
ISBN (Print)9781624105272
DOIs
StatePublished - Jan 1 2018
EventAIAA Information Systems-AIAA Infotech at Aerospace, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Publication series

NameAIAA Information Systems-AIAA Infotech at Aerospace, 2018
Number209989

Other

OtherAIAA Information Systems-AIAA Infotech at Aerospace, 2018
Country/TerritoryUnited States
CityKissimmee
Period1/8/181/12/18

Bibliographical note

Funding Information:
The first two authors would like to acknowledge the support of Honeywell International Inc. and the Robotics, Sensors and Advance Manufacturing MnDrive initiative at the University of Minnesota through the MnDrive Industrial Partnership Grant.

Publisher Copyright:
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

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