Error modeling and calibration of exteroceptive sensors for accurate mapping applications

Underwood, James P, Hill, Andrew, Peynot, Thierry, & Scheding, Steven J (2010) Error modeling and calibration of exteroceptive sensors for accurate mapping applications. Journal of Field Robotics, 27(1), pp. 2-20.

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Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data. © 2009 Wiley Periodicals, Inc.

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28 citations in Web of Science®
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ID Code: 67659
Item Type: Journal Article
Refereed: Yes
Additional Information: 11386147
error modeling
robotic perception
accurate mapping applications
exteroceptive sensor data
spatial error model
generic extrinsic calibration
range-based sensors
sensor location
multisensor data fusion
Keywords: calibration, error analysis, mobile robots, range sensing, laser range finder, radar
DOI: 10.1002/rob.20315
ISSN: 1556-4959
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 06 Mar 2014 01:36
Last Modified: 20 Jul 2017 19:01

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