Abstract
The essential property that every autonomous system should have is the ability to localize itself, i.e., to reason about its location relative to measured landmarks and leverage this information to consistently estimate vehicle location through time. One approach to solving the localization problem is visual place recognition. Using only camera images, this approach has the following goal: during the second traversal through the environment, using only current images, find the match in the database that was created during a previously driven traversal of the same route. Besides the image representation method – in this paper we use feature maps extracted from the OverFeat architecture – for visual place recognition it is also paramount to perform the scene matching in a proper way. For autonomous vehicles and robots traversing through an environment, images are acquired sequentially, thus visual place recognition localization approaches use the structure of sequentiality to locally match image sequences to the database for higher accuracy. In this paper we propose a not only sequential approach to localization; specifically, instead of linearly searching for sequences, we construct a directed acyclic graph and search for any kind of sequences. We evaluated the proposed approach on a dataset consisting of varying environmental conditions and demonstrated that it outperforms the SeqSLAM approach.
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Notes
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We substitute T with \(q_i\) for the sake of clarity.
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Maltar, J., Marković, I., Petrović, I. (2020). NOSeqSLAM: Not only Sequential SLAM. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_15
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