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For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the "ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot. [ABSTRACT FROM AUTHOR]

A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction. [ABSTRACT FROM AUTHOR]