Abstract
This paper presents a real-time viable method for Simultaneous Localization and Mapping (SLAM) using Gaussian mixture models (GMMs) for compute-constrained systems that operate in subterranean environments. The two contributions of this work are (1) a SLAM formulation that uses a GMM-based map representation for pose estimation, mapping and loop closure, and (2) an Expectation Maximization (EM) formulation that significantly reduces the time to learn a GMM from a sensor observation by exploiting the insight that although Gaussian distributions have infinite support, a substantial amount of the support is contained within a finite region. An on-manifold distribution-to-distribution registration approach is used to estimate the pose between consecutive GMMs, and the Cauchy–Schwarz divergence is employed to calculate the difference between the distributions to identify loop closures. The method is evaluated in mine and unstructured cave environments. The results demonstrate superior performance in leveraging the compact representation of the GMM as compared to traditional pose graph SLAM techniques that rely on point cloud-based methods. Further, exploiting the sparsity of the compact support significantly reduces training time toward enabling real-time viability.
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Acknowledgements
The authors would like to thank Aditya Dhawale, Alexander Spitzer, and Kumar Shaurya Shankar for providing feedback on drafts of this manuscript. The authors would also like to thank Curtis Boirum and Brian Osbun for assisting in data collection at Rapps Cave as well as Carroll Bassett of the West Virginia Cave Conservancy for granting access to Rapps Cave.
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Tabib, W., Michael, N. (2021). Simultaneous Localization and Mapping of Subterranean Voids with Gaussian Mixture Models. In: Ishigami, G., Yoshida, K. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-15-9460-1_13
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