An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Constructing 3D City Models by Merging Aerial and Ground Views
IEEE Computer Graphics and Applications
Error modeling and calibration of exteroceptive sensors for accurate mapping applications
Journal of Field Robotics - Three-Dimensional Mapping, Part 3
Continuous 3D scan-matching with a spinning 2D laser
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Automated 3D scenes reconstruction for mobile robots using laser scanning
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
3D laser scan registration of dual-robot system using vision
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
3D mapping for urban service robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Large scale graph-based SLAM using aerial images as prior information
Autonomous Robots
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In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Rényi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.