Fully Automated Laser Range Calibration

Abstract

We present a novel method for fully automated exterior calibration of a 2D scanning laser range sensor that attains accurate pose with respect to a fixed 3D reference frame. This task is crucial for applications that attempt to recover self-consistent 3D environment maps and produce accurately registered or fused sensor data.A key contribution of our approach lies in the design of a class of calibration target objects whose pose can be reliably recognized from a single observation (i.e. from one 2D range data stripe). Unlike other techniques, we do not require simultaneous camera views or motion of the sensor, making our approach simple, flexible and environment-independent.In this paper we illustrate the target geometry and derive the relationship between a single 2D range scan and the 3D sensor pose. We describe an algorithm for closed-form solution of the 6 DOF pose that minimizes an algebraic error metric, and an iterative refinement scheme that subsequently minimizes geometric error. Finally, we report performance and stability of our technique on synthetic and real data sets, and demonstrate accuracy within 1 degree of orientation and 3 cm of position in a realistic configuration.

Lidar attitude estimation for vehicle safety systems

“…Lidar is an ideal measurement sensor for vehicle safety systems because lidars are capable of quickly making dense, accurate, and precise distance and reflectivity measurements, often in three dimensions. It is necessary to calibrate a lidar so that it aligns with some common coordinate system, especially in the case of multi-sensor systems so that these sensors can be related to one another in a meaningful way [3]. Additionally, this calibration process can help mitigate measurement errors, allowing the lidar algorithm to report more accurate measurements.…”

“…Hence, the determination or compensation of the yaw, pitch, and roll of the lidar in reference to the vehicle's axes will constitute a calibrated lidar. Calibration techniques vary greatly, but often require the scanning of an object of known geometry [3], [1], a beacon or landmark [8], [5], or a planar surface [4], [9], [2], [6]. These calibration techniques additionally vary in how many degrees of freedom they calibrate to.…”

“…For this to be accomplished the sensor's reference frame must be known relative to the vehicle's reference frame, thus sensor measurements can be related in a meaningful way to the vehicle. It is common for sensors such as light detection and ranging (lidar) systems to not be mounted with perfect alignment to the vehicle's coordinate frame, therefore it is necessary to be capable of determining how the lidar's coordinate frame relates to the vehicle's coordinate frame which is determined through a process known as calibration [3]. Lidar calibration can vary greatly in complexity and technique, but to be able to fully relate the sensor's coordinate frame to the vehicle's, the pitch, roll, and yaw of the lidar relative to the vehicle must be determined or compensated for in some way.…”

Range Information Characterization of the Hokuyo UST-20LX LIDAR Sensor

“…This can be done in numerous ways, and can use more than one type of sensor. Some common extrinsic calibration procedures use a LiDAR-Camera procedure as outlined in [7][8][9][10], and multiple LiDAR sensors or multiple sensor views as illustrated by [11][12][13][14][15][16], of a fixed target structure for a faster extrinsic calibration prior to operations [17][18][19][20]. These scenarios require the sensors raw data to be correlated into one coherent picture.…”

An Integrated Flexible Self-calibration Approach for 2D Laser Scanning Range Finders Applied to the Hokuyo UTM-30LX-EW

“…The registration of single measurement stripes can for example be realized by GPS/INS integration (Maier and Kleiner, 2010), by 3D visual SLAM with a camera (Georgiev and Allen, 2004) or by attaining accurate sensor pose with respect to a fixed 3D reference frame (Antone and Friedman, 2007). Considering lever-arm and bore-sight effects finally results in a 3D point cloud representation of the object space.…”