Abstract
Systems composed of multiple sensors for exteroceptive perception are becoming increasingly common, such as mobile robots or highly monitored spaces. However, to combine and fuse those sensors to create a larger and more robust representation of the perceived scene, the sensors need to be properly registered among them, that is, all relative geometric transformations must be known. This calibration procedure is challenging as, traditionally, human intervention is required in variate extents. This paper proposes a nearly automatic method where the best set of geometric transformations among any number of sensors is obtained by processing and combining the individual pairwise transformations obtained from an experimental method. Besides eliminating some experimental outliers with a standard criterion, the method exploits the possibility of obtaining better geometric transformations between all pairs of sensors by combining them within some restrictions to obtain a more precise transformation, and thus a better calibration. Although other data sources are possible, in this approach, 3D point clouds are obtained by each sensor, which correspond to the successive centers of a moving ball its field of view. The method can be applied to any sensors able to detect the ball and the 3D position of its center, namely, LIDARs, mono cameras (visual or infrared), stereo cameras, and TOF cameras. Results demonstrate that calibration is improved when compared to methods in previous works that do not address the outliers problem and, depending on the context, as explained in the results section, the multi-pairwise technique can be used in two different methodologies to reduce uncertainty in the calibration process.
Highlights
Modern robots count on a wealth of sensors for many essential operations that need perception such as representation, obstacle avoidance, planning, guidance, localization, and most of the tasks generically related to navigation and safety
The proposal that is going to be detailed in the paper has the following advantages; (i) it can be applied to any sensor that is capable of detecting the 3D position of a moving target, and cameras; (ii) it can take all transformation paths into account or a subset of paths; (iii) it is faster than iterative methods with deterministic duration and computational cost; and (iv) in certain conditions, it allows to reduce calibration errors in a straightforward algorithmic process
The previous section presented an analysis of uncertainty propagation using a case study for more clarity, but demonstrated that, in some conditions, the multi-pairwise approach reduces the uncertainty in calculating the geometric transformations, by analyzing the propagated uncertainty in final orientations and translations of the several sensors relative localization, which is the essence of extrinsic calibration
Summary
Modern robots count on a wealth of sensors for many essential operations that need perception such as representation, obstacle avoidance, planning, guidance, localization, and most of the tasks generically related to navigation and safety. Numerous works exist to calculate the extrinsic parameters of cameras or image based or image reducible sensors External devices such as chessboards, charuco boards, or others are used to create a known real-world pattern of points or geometric feature which are traceable on the respective images. The main contribution of this paper is a technique to perform the extrinsic calibration of multiple sensors by eliminating outliers in the experimental process and by combining individual pairwise transformations It improves a previously developed technique based on pairwise geometric transformations obtained from different point clouds (one for each sensor) generated with the successive center points of a moving ball. The paper is divided in the following main sections; the related work, the proposed approach that includes the main algorithms described in detail, results from simulated and real data experiments, and final conclusions and future perspectives
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