Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.
More recently, Sayem @cite_14 proposed a Fast-SeqSLAM method, which improved the searching efficiency by utilizing an approximate nearest neighbor (ANN) as the initial estimate for potential matches. Since ANN in Fast-SeqSLAM still relies on single image feature similarities, the initial search efficiency may decrease when the original matching frame sequence is of a relatively long-time scale. Liu and Zhang @cite_12 applied a particle filter to improve the matching efficiency, where each particle represented a potential subset of the frame sequence @cite_11 . Rather than evaluating the whole frame sequence, they predicted the weights of multiple particles based on frame sequence similarities and the robot motion. However, both the methods described above require a good estimation of the initial matched location.
@cite_14 @cite_12 @cite_11
2737165983 2025199671 2124156864
Loop closure detection or place recognition is a fundamental problem in robot simultaneous localization and mapping (SLAM). SeqSLAM is considered to be one of the most successful algorithms for loop closure detection as it has been demonstrated to be able to handle significant environmental condition changes including those due to illumination, weather, and time of the day. However, SeqSLAM relies heavily on exhaustive sequence matching, a computationally expensive process that prevents the algorithm from being used in dealing with large maps. In this paper, we propose Fast-SeqSLAM, an efficient version of SeqSLAM. Fast-SeqSLAM has a much reduced time complexity without degrading the accuracy, and this is achieved by using an approximate nearest neighbor (ANN) algorithm to match the current image with those in the robot map and extending the idea of SeqSLAM to greedily search a sequence of images that best match with the current sequence. We demonstrate the effectiveness of our Fast-SeqSLAM algorithm in appearance based loop closure detection. We propose a method in this paper to perform sequence-based appearance SLAM in an efficient and effective way. Sequence-based SLAM (or SeqSLAM for short) makes use of the image descriptors extracted from a series of consecutive frames and matching is done between two such image sequences. It has been shown to be effective in dealing with significant illumination change where localization and mapping can be conducted under different time periods and weather conditions. To address the computational issue that can arise from the exhaustive search of the candidate sequences with the increase of map size, we use a particle filter to implement the Bayes filtering framework of estimating the true match. The resampling of the particles allows us to maintain only a small number of hypotheses while still capturing the true distribution of the robot location. Our method is highly scalable and efficient, validated on a large dataset with comparable results to the original algorithm in terms of performance. In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.