A non-iterative and effective procedure for simultaneous odometry and camera calibration for a differential drive mobile robot based on the singular value decomposition

Automatic Calibration of Odometry and Robot Extrinsic Parameters Using Multi-Composite-Targets for a Differential-Drive Robot with a Camera

This paper simultaneously calibrates odometry parameters and the relative pose between a monocular camera and a robot automatically. Most camera pose estimation methods use natural features or artificial landmark tools. However, there are mismatches and scale ambiguity for natural features; the large-scale precision landmark tool is also challenging to make. To solve these problems, we propose an automatic process to combine multiple composite targets, select keyframes, and estimate keyframe poses. The composite target consists of an aruco marker and a checkerboard pattern. First, an analytical method is applied to obtain initial values of all calibration parameters; prior knowledge of the calibration parameters is not required. Then, two optimization steps are used to refine the calibration parameters. Planar motion constraints of the camera are introduced in these optimizations. The proposed solution is automatic; manual selection of keyframes, initial values, and robot construction within a specific trajectory are not required. The competing accuracy and stability of the proposed method under different target placements and robot paths are tested experimentally. Positive effects on calibration accuracy and stability are obtained when (1) composite targets are adopted; (2) two optimization steps are used; (3) plane motion constraints are introduced; and (4) target numbers are increased.

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

This paper proposes a method that automatically calibrates four cameras of an around view monitor (AVM) system in a natural driving situation. The proposed method estimates orientation angles of four cameras composing the AVM system, and assumes that their locations and intrinsic parameters are known in advance. This method utilizes lane markings because they exist in almost all on-road situations and appear across images of adjacent cameras. It starts by detecting lane markings from images captured by four cameras of the AVM system in a cost-effective manner. False lane markings are rejected by analyzing the statistical properties of the detected lane markings. Once the correct lane markings are sufficiently gathered, this method first calibrates the front and rear cameras, and then calibrates the left and right cameras with the help of the calibration results of the front and rear cameras. This two-step approach is essential because side cameras cannot be fully calibrated by themselves, due to insufficient lane marking information. After this initial calibration, this method collects corresponding lane markings appearing across images of adjacent cameras and simultaneously refines the initial calibration results of four cameras to obtain seamless AVM images. In the case of a long image sequence, this method conducts the camera calibration multiple times, and then selects the medoid as the final result to reduce computational resources and dependency on a specific place. In the experiment, the proposed method was quantitatively and qualitatively evaluated in various real driving situations and showed promising results.