In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration. Our approach is an extension of hand-eye calibration framework to 2D-3D calibration. By using the sensor fusion odometry method, the scaled camera motions are calculated with high accuracy. In addition to this, we clarify the suitable motion for this calibration method. The proposed method only requires the three-dimensional point cloud and the camera image and does not need other information such as reflectance of LiDAR and to give initial extrinsic parameter. In the experiments, we demonstrate our method using several sensor configurations in indoor and outdoor scenes to verify the effectiveness. The accuracy of our method achieves more than other comparable state-of-the-art methods.
The method of changing the position and orientation of the sensor and performing the calibration using the motions observed by each sensor is known as hand-eye calibration. Let @math and @math are the changes in position and orientation observed by two fixed sensors respectively, and @math be the unknown relative position and orientation between sensors. Then the expression @math holds(Refer top of Fig. ). By using this expression to solve @math , extrinsic parameter between sensors can be obtained @cite_7 @cite_4 . Furthermore, due to the influence of noise on the sensor, Kalman Filter was used in calibration methods for estimating the bias of the sensor simultaneously @cite_25 @cite_0 .
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Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the cameraâIMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure. In this paper, the matrix equation AX=XB used for hand to sensor calibration of robot-mounted sensors is analyzed using a geometrical approach. The analysis leads to an original way to describe the properties of the equation and to find all of its solutions. It will also be highlighted why, when multiple instances AiX=XBi (i=1,2,...) of the equation are to be solved simultaneously, the system is overconstrained. Finally, singular cases are also discussed. © 2005 Wiley Periodicals, Inc. To use a wrist-mounted sensor (such as a camera) for a robot task, the position and orientation of the sensor with respect to the robot wrist frame must be known. The sensor mounting position can be found by moving the robot and observing the resulting motion of the sensor. This yields a homogeneous transform equation of the form AX=XB, where A is the change in the robot wrist position, B is the resulting sensor displacement, and X is the sensor position relative to the robot wrist. The solution to an equation of this form has one degree of rotational freedom and one degree of translation freedom if the angle of rotation of A is neither 0 nor pi radians. To solve for X uniquely, it is necessary to make two arm movements and form a system of two equations of the form: A sub 1 X=XB sub 1 and A sub 2 X=XB sub 2 . A closed-form solution to this system of equations is developed and the necessary conditions for uniqueness are stated. >