Fast and Memory Efficient Graph Optimization via ICM for Visual Place Recognition

Stefan Schubert (TU Chemnitz),
Peer Neubert (TU Chemnitz),
Peter Protzel (TU Chemnitz)
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Paper #091
Interactive Poster Session III Interactive Poster Session VI

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Visual place recognition is the task of finding same places in a set of database images for a given set of query images. This task becomes particularly challenging if the environmental condition changes between database and query, for example from day to night. In this paper, we build upon our recent work on graph optimization for place recognition, where a graph was used to model additional structural knowledge like sequences. A subsequent non-linear least squares optimization (NLSQ) improved the place recognition performance. While this approach achieves very high performance, it is quite slow and memory inefficient. This paper addresses the long runtime and the high memory usage in order to obtain the same or better place recognition performance faster on larger problems. We propose a novel graph optimization procedure that is based on Iterated Conditional Modes (ICM). In addition, we investigate a new cost function for an edge in the graph. Our novel ICM-based approach achieves 9.1msec maximum runtime per query, which is 260x faster than the minimum runtime with NLSQ. Moreover, with ICM we can optimize problems that are not feasible with NLSQ on a full graph due to memory limitations. To demonstrate the superior performance of our ICM-based method, we provide extensive experimental evaluations with the essence of 987 precision-recall curves: Our proposed ICM-based method is compared to the NLSQ-based method as well as to six sequence-based approaches from the literature on 21 sequence combinations from five datasets with four different image descriptors. Our experiments show that our ICM-based method with sequence-exploitation not only improves the NLSQ-based performance by 10% on average while being 385x faster and using more than 60x less memory. It also significantly outperforms all six sequence-based methods from the literature by at least 32% on average with the NetVLAD descriptor while using comparable runtime and memory. Code is available online.

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