MCC-based Back-end Optimization Method and Its Application in ORB-SLAM2
Autonomous localization and environment awareness are prerequisites for robots to achieve complex tasks,and vision simultaneous localization and mapping(VSLAM)technology is an effective solution.In VSLAM,sensor errors and environmental noise,etc.,affect the localization and mapping accuracy,resulting in cumulative errors.Back-end optimization plays a key role in eliminating the accumulated error in VSLAM.Existing back-end optimization algorithms are usually premised on Gaussian noise and belong to the back-end algorithms as per the MSE standard.However,due to the non-convex nature of images and non-Gaussian noise generated in real scenes,the Gaussian noise assumption does not always valid,leading to performance degradation of existing algorithms when running in real scenes.In view of this,a back-end optimization method based on the MCC criterion is proposed by taking advantage of the maximum correlation entropy(MCC)criterion in dealing with non-Gaussian noise,and the proposed method is applied to the ORB-SLAM2 framework to test the performance of the proposed method in terms of localiza-tion and image building accuracy.Finally,experiments are conducted on EuRoC and KITTI public datasets,and the experimental results show that the proposed method outperforms the Huber-based back-end optimization algorithm as well as the Cauchy-based back-end optimization algorithm in the original ORB-SLAM2 for the majority of sequences,both indoor and outdoor.
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