A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Robot Motion Planning and Control
Robot Motion Planning and Control
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports
Journal of Field Robotics - Special Issue on Space Robotics
Fast Laser Scan Matching using Polar Coordinates
International Journal of Robotics Research
Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM
International Journal of Robotics Research
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Model based vehicle detection and tracking for autonomous urban driving
Autonomous Robots
Practical visual odometry for car-like vehicles
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Mobile robot simultaneous localization and mapping in unstructured environments
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Correlation-based visual odometry for ground vehicles
Journal of Field Robotics
Performance evaluation of 1-point-RANSAC visual odometry
Journal of Field Robotics
Metric-based iterative closest point scan matching for sensor displacement estimation
IEEE Transactions on Robotics
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This paper presents a technique to estimate in real time the egomotion of a vehicle based solely on laser range data. This technique calculates the discrepancy between closely spaced two-dimensional laser scans due to the vehicle motion using scan matching techniques. The result of the scan alignment is converted into a nonlinear motion measurement and fed into a nonholonomic extended Kalman filter model. This model better approximates the real motion of the vehicle when compared to more simplistic models, thus improving performance and immunity to outliers. The motion estimate is intended to be used for egomotion compensation in a target-tracking algorithm for situation awareness applications. In this paper, several recent scan matching algorithms were evaluated for their accuracy and computational speed: metric-based iterative closest point (MbICP), point-to-line ICP (PIICP), and polar scan matching. The proposed approach is performed in real time and provides an accurate estimate of the current robot motion. The MbICP algorithm proved to be the most advantageous scan matching algorithm, but it is still comparable to PlICP. The motion estimation algorithm is validated through experimental testing in real world conditions. © 2013 Wiley Periodicals, Inc.