Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 7, Issue 1, Pages 287-294Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3125046
Keywords
SLAM; Mapping
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This letter presents a novel hierarchical algorithm HiPE for pose graph initialization, which utilizes a sparse graph construction and maximum likelihood estimates to achieve non-linear initialization and guide the fine-grained optimization process of the final solution. Experimental results show that HiPE leads to a more efficient and robust optimization process compared to existing methods.
Pose graph optimization is a non-convex optimization problem encountered in many areas of robotics perception. Its convergence to an accurate solution is conditioned by two factors: the non-linearity of the cost function in use and the initial configuration of the pose variables. In this letter, we present HiPE, a novel hierarchical algorithm for pose graph initialization. Our approach exploits a coarse-grained graph that encodes an abstract representation of the problem geometry. We construct this graph by combining maximum likelihood estimates coming from local regions of the input. By leveraging the sparsity of this representation, we can initialize the pose graph in a non-linear fashion, without computational overhead compared to existing methods. The resulting initial guess can effectively bootstrap the fine-grained optimization that is used to obtain the final solution. In addition, we perform an empirical analysis on the impact of different cost functions on the final estimate. Our experimental evaluation shows that the usage of HiPE leads to a more efficient and robust optimization process, comparing favorably with state-of-the-art methods.
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