Simultaneous Calibration of Odometry and Head-Eye Parameters for Mobile Robots with a Pan-Tilt Camera

Abstract

In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to be estimated for more precise operation. However, the odometric and head-eye parameters are typically estimated separately, which is an inconvenience and requires longer calibration time. Even though several researchers have proposed simultaneous calibration methods that obtain both odometric and head-eye parameters simultaneously to reduce the calibration time, they are only applicable to a mobile robot with a fixed camera mounted, not for mobile robots equipped with a pan-tilt motorized camera systems, which is a very common configuration and widely used for wide view. Previous approaches could not provide the z-axis translation parameter between head-eye coordinate systems on mobile robots equipped with a pan-tilt camera. In this paper, we present a full simultaneous mobile robot calibration of head–eye and odometric parameters, which is appropriate for a mobile robot equipped with a camera mounted on the pan-tilt motorized device. After a set of visual features obtained from a chessboard or natural scene and the odometry measurements are synchronized and received, both odometric and head-eye parameters are iteratively adjusted until convergence prior to using a nonlinear optimization method for more accuracy.

Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

“…Therefore, these methods require excellent orientation estimations, which is complicated to guarantee with cost-effective sensors outdoors. The method is improved with an iterative loop in [ 33 ] to increase the convergence to the true parameter values, however it is an indoor application for mobile robots. Another simplification of the regression problem can be found in [ 18 ], where the nonlinear estimation problem is handled with linearized system dynamics and integrated prediction error minimization.…”

“…However, because the predictor in this regression problem is a dynamic system model (the odometry model) the measurement uncertainty has a high impact. At the beginning of the section, some possible handlings of the problems are mentioned in the presented works [ 18 , 31 , 32 , 33 ] in the field of mobile robots. Due to the improper orientation measurements in the outdoors and the complexity of our odometry model other improvement is necessary.…”

“…[ 31 ], and also upgraded in ref. [ 33 ] with the addition of an iterative loop. In [ 34 ] the general nonlinear least square problem is handled with the Gauss–Newton approximation and the observability of the calibration parameters is also taken into account.…”