Abstract. Rotating multi-beam LIDARs mounted on moving platforms have become very successful for many applications such as autonomous navigation, obstacle avoidance or mobile mapping. To obtain accurate point coordinates, a precise calibration of such a LIDAR system is required. For the determination of the corresponding parameters we propose a calibration scheme which exploits the information of 3D reference point clouds captured by a terrestrial laser scanning (TLS) device. It is assumed that the accuracy of this point clouds is considerably higher than that from the multi-beam LIDAR and that the data represent faces of man-made objects at different distances. After extracting planes in the reference data sets, the point-plane-incidences of the measured points and the reference planes are used to formulate the implicit constraints. We inspect the Velodyne HDL-64E S2 system as the best-known representative for this kind of sensor system. The usability and feasibility of the calibration procedure is demonstrated with real data sets representing building faces (walls, roof planes and ground). Beside the improvement of the point accuracy by considering the calibration results, we test the significance of the parameters related to the sensor model and consider the uncertainty of measurements w.r.t. the measured distances. The Velodyne returns two kinds of measurements – distances and encoder angles. To account for this, we perform a variance component estimation to obtain realistic standard deviations for the observations.
[1]
Derek D. Lichti,et al.
Temporal Stability of the Velodyne HDL-64E S2 Scanner for High Accuracy Scanning Applications
,
2011,
Remote. Sens..
[2]
Sebastian Thrun,et al.
Unsupervised Calibration for Multi-beam Lasers
,
2010,
ISER.
[3]
Derek D. Lichti,et al.
Static Calibration and Analysis of the Velodyne HDL-64E S2 for High Accuracy Mobile Scanning
,
2010,
Remote. Sens..
[4]
Dimitrios G. Kottas,et al.
3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization
,
2012,
Int. J. Robotics Res..
[5]
Radu Bogdan Rusu,et al.
3D is here: Point Cloud Library (PCL)
,
2011,
2011 IEEE International Conference on Robotics and Automation.
[6]
Martin Magnusson,et al.
The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection
,
2009
.
[7]
Andrew Zisserman,et al.
Multiple View Geometry
,
1999
.
[8]
Simon Lacroix,et al.
Calibration of a rotating multi-beam lidar
,
2010,
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[9]
Karl-Rudolf Koch,et al.
Parameter estimation and hypothesis testing in linear models
,
1988
.