基于MCC的后端优化方法及其在ORB-SLAM2中的应用

MCC-based Back-end Optimization Method and Its Application in ORB-SLAM2

自主定位和环境感知是机器人实现复杂任务的前提,视觉同时定位与建图(VSLAM)技术是有效解决方案。VSLAM中,传感器误差和环境噪声等影响定位和建图精度,造成累计误差。后端优化是VSLAM中消除累计误差的关键环节,现有后端优化算法通常以高斯噪声为前提,属于MSE标准下的后端算法。然而,由于图像的非凸特性和真实场景中产生的非高斯噪声,高斯噪声假设并不总成立,导致现有算法在真实场景中运行时性能下降。鉴于此,利用最大互相关熵(MCC)标准在处理非高斯噪声方面的优势,提出了一种基于MCC标准的后端优化方法,并将所提出方法应用于ORB-SLAM2框架,以测试所提出的方法在定位和建图精度方面的性能。最后,在EuRoC和KITTI公开数据集上进行实验,结果表明,无论是室内还是室外,所提方法在大部分序列中性能优于原ORB-SLAM2中基于Huber的后端优化算法以及基于Cauchy的后端优化算法。

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 localization 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.