Abstract
LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. <br><br> A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. <br><br> In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. <br><br> An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.
Highlights
Light Detection and Ranging (LIDAR) sensors are useful for many tasks: mapping (Nuchter et al, 2004), localization
This paper is organized as follow: in section 2., we present the state of the art concerning the algorithms for the intrinsic calibration of multi-beam LIDAR systems
Because the intrinsic calibration parameters are optimal - this is the way the simulated data are constructed, we added some biases to the calibration parameters, and compared the results between the optimization of the extrinsic calibration parameters only, and the optimization of all the calibration parameters
Summary
Light Detection and Ranging (LIDAR) sensors are useful for many tasks: mapping (Nuchter et al, 2004), localization Multi-beam LIDAR sensors give data with a high density of points and are more precise than mono-beam sensors: they are evolving fast, and become cheaper with time. The intrinsic calibration describe the transformation of the acquired from spherical coordinates to cartesian coordinates, referenced in the same reference frame. A beam is set as a reference, and we optimize the intrinsic calibration parameters of the other beams regarding this reference. We will call calibration of the sensor the intrinsic calibration of the multi-beam LIDAR sensor: the intrinsic calibration of the sensor allows to have data correctly referenced in the cartesian sensor reference frame. This paper is organized as follow: in section 2., we present the state of the art concerning the algorithms for the intrinsic calibration of multi-beam LIDAR systems.
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