Multi-beam LIDAR Sensor Research Articles

A MATHEMATICAL SENSOR MODEL FOR INDOOR USE OF A MULTI-BEAM ROTATING 3D LIDAR

Abstract. Our contribution presents a new perspective in the mathematical description of a rotating multi-beam LiDAR sensor, in a sense that we make use of projective geometry along with the “homogeneous general equation of the second degree” to parametrize scan lines. We describe the scan geometry of a typical multi-beam rotating 3D LiDAR by representing scan lines as pojective conics that represent a projective figure (a cone) in an embedding plane. This approach enables the parameterization of each scan line using a generic conic section equation. Most modeling approachs model spinning LiDAR sensors in terms of individual points sampled by a laser beam. On the contrary, we propose a model that provides a high-level geometric interpretation both for the environment and the laser scans. Possible application scenarios include exterior and interior calibration of multiple rotating multi-beam sensors, scan distortion correction and localization in planar maps.

Point cloud refinement with self‐calibration of a mobile multibeam lidar sensor

AbstractLidar sensors are widely used in mobile mapping systems. With recent developments, these sensors provide large volumes of data which are necessary for some applications that require a high level of detail. Multibeam lidar sensors can provide this level of detail, but need a specific calibration routine to provide the best precision possible. Because they have multiple beams, the calibration of such sensors is difficult and is not well represented in the literature. This work presents an automatic method for the optimisation of the calibration parameters of a multibeam lidar sensor mounted on a mobile platform. The proposed approach does not require any calibration target, and only uses information from the acquired point clouds, which makes it simple to use. The goal of the optimisation is to find calibration parameters that will improve the structure of the data. At the end of the automatic process, a confidence value is provided for the calibration parameters found.

POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

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.

POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

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.

TARGET-FREE EXTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM

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 literature. 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.