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ISPRS Int. J. Geo-Inf. 2015, 4(3), 1301-1316; doi:10.3390/ijgi4031301

Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data

1
Civil, Environmental and Geodetic Engineering, The Ohio State University
2
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Stephan Winter, Alper Yilmaz, Monika Sester and Wolfgang Kainz
Received: 31 January 2015 / Revised: 13 June 2015 / Accepted: 20 July 2015 / Published: 31 July 2015
(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)
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Abstract

Monitoring vehicular road traffic is a key component of any autonomous driving platform. Detecting moving objects, and tracking them, is crucial to navigating around objects and predicting their locations and trajectories. Laser sensors provide an excellent observation of the area around vehicles, but the point cloud of objects may be noisy, occluded, and prone to different errors. Consequently, object tracking is an open problem, especially for low-quality point clouds. This paper describes a pipeline to integrate various sensor data and prior information, such as a Geospatial Information System (GIS) map, to segment and track moving objects in a scene. We show that even a low-quality GIS map, such as OpenStreetMap (OSM), can improve the tracking accuracy, as well as decrease processing time. A bank of Kalman filters is used to track moving objects in a scene. In addition, we apply non-holonomic constraint to provide a better orientation estimation of moving objects. The results show that moving objects can be correctly detected, and accurately tracked, over time, based on modest quality Light Detection And Ranging (LiDAR) data, a coarse GIS map, and a fairly accurate Global Positioning System (GPS) and Inertial Measurement Unit (IMU) navigation solution. View Full-Text
Keywords: LiDAR; tracking; sensor; integration; GIS; autonomous driving LiDAR; tracking; sensor; integration; GIS; autonomous driving
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hosseinyalamdary, S.; Balazadegan, Y.; Toth, C. Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data. ISPRS Int. J. Geo-Inf. 2015, 4, 1301-1316.

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