Calibration of a rotating multi-beam lidar

Muhammad, Naveed; Lacroix, Simon

doi:10.1109/iros.2010.5651382

Cited by 100 publications

(60 citation statements)

References 6 publications

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“…Indeed, (Glennie and Lichti, 2010) and (Muhammad and Lacroix, 2010) propose an optimization of the intrinsic parameters for the 64-beam version, and (Chan and Lichti, 2013) proposes an intrinsic calibration for the 32-beam model. In (Glennie and Lichti, 2010) and (Muhammad and Lacroix, 2010), the authors use a particular calibration environment to optimize the intrinsic parameters, which contains many planar walls: these walls are extracted from the acquired point cloud, and their structure is corrected in order to optimize the calibration parameters. In (Chan and Lichti, 2013), the optimization of the intrinsic parameters is done statically, by using environment information such as planar wall and vertical cylinders.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Refinement With a Target-Free Intrinsic Calibration of a Mobile Multi-Beam Lidar System

Nouiraa

Deschaud

Goulettea

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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. 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. 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. 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.

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Section: Related Workmentioning

confidence: 99%

Point Cloud Refinement With a Target-Free Intrinsic Calibration of a Mobile Multi-Beam Lidar System

Nouiraa

Deschaud

Goulettea

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This calibration is then used to epipolar rectify the images prior to stereo reconstruction. Calibration results are shown in The geometric model for the LIDAR allows for systematic translational and rotational offsets, similar to (Muhammad and Lacroix, 2010). Five parameters are required: two for translational offsets orthogonal to the laser beam axis, two for rotational offsets with respect to the reported position of the mount and a distance offset.…”

Section: System Geometry and Modelmentioning

confidence: 99%

Data Fusion of Lidar Into a Region Growing Stereo Algorithm

Veitch-Michaelis

Müller

Storey

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Stereo vision and LIDAR continue to dominate standoff 3D measurement techniques in photogrammetry although the two techniques are normally used in competition. Stereo matching algorithms generate dense 3D data, but perform poorly on low-texture image features. LIDAR measurements are accurate, but imaging requires scanning and produces sparse point clouds. Clearly the two techniques are complementary, but recent attempts to improve stereo matching performance on low-texture surfaces using data fusion have focused on the use of time-of-flight cameras, with comparatively little work involving LIDAR.A low-level data fusion method is shown, involving a scanning LIDAR system and a stereo camera pair. By directly imaging the LIDAR laser spot during a scan, unique stereo correspondences are obtained. These correspondences are used to seed a regiongrowing stereo matcher until the whole image is matched. The iterative nature of the acquisition process minimises the number of LIDAR points needed. This method also enables simple calibration of stereo cameras without the need for targets and trivial coregistration between the stereo and LIDAR point clouds. Examples of this data fusion technique are provided for a variety of scenes.

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“…If we take a look at the Velodyne sensor, some intrinsic calibration methods exist, like (Glennie and Lichti, 2010) and (Muhammad and Lacroix, 2010), which propose an optimization of the intrinsic parameters of the 64-beam version, or (Chan and Lichti, 2013), which proposes an intrinsic calibration for the 32-beam model. In this paper, we are only interested in the extrinsic calibration of multi-beam LIDAR sensors.…”

Section: Related Workmentioning

confidence: 99%

Target-Free Extrinsic Calibration of a Mobile Multi-Beam Lidar System

Nouira

Deschaud

Goulette

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:LIDAR sensors are widely used in mobile mapping systems. With the recent developments, the sensors provide high amounts of data, which are necessary for some applications that require a high level of detail. Multi-beam LIDAR sensors can provide this level of detail, but need a specific calibration routine to provide the best precision possible. Because they have many beams, the calibration of such sensors is difficult and is not well represented in the litterature. We present an automatic method for the optimization of the calibration parameters of a multi-beam LIDAR sensor: the proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple to use. For our optimization method, we define an energy function which penalizes points far from local planar surfaces. At the end of the automatic process, we are able to give the precision of the calibration parameters found.

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Calibration of a rotating multi-beam lidar

Cited by 100 publications

References 6 publications

Point Cloud Refinement With a Target-Free Intrinsic Calibration of a Mobile Multi-Beam Lidar System

Point Cloud Refinement With a Target-Free Intrinsic Calibration of a Mobile Multi-Beam Lidar System

Data Fusion of Lidar Into a Region Growing Stereo Algorithm

Target-Free Extrinsic Calibration of a Mobile Multi-Beam Lidar System

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