Simultaneous Calibration of Odometry and Sensor Parameters for Mobile Robots

Abstract

Consider a differential-drive mobile robot equipped with an on-board exteroceptive sensor that can estimate its own motion, e. g., a range-finder. Calibration of this robot involves estimating six parameters: three for the odometry (radii and distance between the wheels) and three for the pose of the sensor with respect to the robot. After analyzing the observability of this problem, this paper describes a method for calibrating all parameters at the same time, without the need for external sensors or devices, using only the measurement of the wheel velocities and the data from the exteroceptive sensor. The method does not require the robot to move along particular trajectories. Simultaneous calibration is formulated as a maximum-likelihood problem and the solution is found in a closed form. Experimental results show that the accuracy of the proposed calibration method is very close to the attainable limit given by the Cramer-Rao bound

Simultaneous Parameter Calibration, Localization, and Mapping

“…Both cases are pathologic and can easily be avoided by varying the wheel velocities of the robot. Recently, Censi et al [28] gave a proof that our informal statement holds. They show that two linear independent velocity commands which lead to two circular arcs witch different radii are sufficient to guarantee the observability of the parameters.…”

Global calibration of non-overlapping cameras: State of the art

“…Optical mirrors [34,35], [43], [47], [49], [51] [39] [ 36], [40], [48] [ 37], [45,46], [50], [52] Structure from motion [55], [57], [59][60][61] [ 53,54], [63], [66,67], [69][70][71] [56], [58], [62], [64,65], [68], [72,73] Laser projection [77,78], [80] [74-76] Close range photogrammetry [87] [ [88][89][90] range of applications in visual tracking, pose estimation and robot navigation. The method based on laser projection can theoretically work in a large range, but it is difficult to obtain high calibration accuracy because the image quality of the spot or the light plane decays as the work distance increases.…”

Bootstrapping bilinear models of Simple Vehicles

“…For mobile robots, the parameters to identify can be typically divided in three sets: the set of odometric parameters (e.g., the wheels radii); the set of extrinsic parameters, which describe the relative transformation reference frames (e.g., the base-to-sensor transform); and the set of each sensor's intrinsic parameters (e.g., a camera's focal length). For a recent overview of calibration techniques for mobile robots, see (Censi, Franchi, Marchionni, and Oriolo, 2013). These techniques assume to know the type of sensors and actuators being calibrated, which define the family of models being considered.…”

“…In contrast, traditional calibration techniques usually assume that the semantics of the data is already known. For example, one might assume that the relation between the motion of the sensor and the variation in the range data is already known, and this relation can be inverted to obtain the motion of the sensor through a scan-matching procedure (Censi, Franchi, Marchionni, and Oriolo, 2013).…”