Abstract
Multi-sensor perception systems may have mismatched coordinates between each sensor even if the sensor coordinates are converted to a common coordinate. This discrepancy can be due to the sensor noise, deformation of the sensor mount, and other factors. These mismatched coordinates can seriously affect the estimation of a distant object’s position and this error can result in problems with object identification. To overcome these problems, numerous coordinate correction methods have been studied to minimize coordinate mismatching, such as off-line sensor error modeling and real-time error estimation methods. The first approach, off-line sensor error modeling, cannot cope with the occurrence of a mismatched coordinate in real-time. The second approach, using real-time error estimation methods, has high computational complexity due to the singular value decomposition. Therefore, we present a fast online coordinate correction method based on a reduced sensor position error model with dominant parameters and estimate the parameters by using rapid math operations. By applying the fast coordinate correction method, we can reduce the computational effort within the necessary tolerance of the estimation error. By experiments, the computational effort was improved by up to 99.7% compared to the previous study, and regarding the object’s radar the identification problems were improved by 94.8%. We conclude that the proposed method provides sufficient correcting performance for autonomous driving applications when the multi-sensor coordinates are mismatched.
Highlights
In autonomous driving systems, one of the most important factors to plan and control a vehicle’s motion is the understanding of the autonomous vehicle’s environment, especially when moving objects approach it
This paper proposed the fast online coordinate correction method with reduced computational effort for matching the slight mismatched sensor coordinates
The advantages of this study can be summarized as follows: The proposed method has lower computational complexity than the conventional methods, such as iterative closest point (ICP), least square method (LSM), and other methods, because the proposed method can estimate the parameters of the under-determined system by using rapid mathematical operations, unlike previous studies that require the pseudo inverse operation or singular value decomposition (SVD)
Summary
One of the most important factors to plan and control a vehicle’s motion is the understanding of the autonomous vehicle’s environment, especially when moving objects approach it. Another advantage is that the corrected object information could be obtained without an additional computation effort to estimate the model parameters since the model parameters are already estimated off-line This method requires an expensive equipment, the RTK-GPS, to estimate the object information error and could not cope with the coordinate mismatch occurring in real-time. Another approach is minimizing the position error between sensor measurements based on the error estimation methods, such as iterative closest point (ICP), least square method (LSM), amongst other methods [17,18,19,20,21].
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