Abstract
To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-of-the-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.
Highlights
Autonomous underwater vehicles (AUVs) have been used for a variety of tasks, including oceanographic surveys, demining, and seabed mappings [1]
Contribution: In the proposed Particle Filter (PF)-bathymetric simultaneous localization and mapping (BSLAM) method, the whole trajectory of the vehicle is estimated in two parts: particles only keep the vehicle’s current estimate positions to reduce the storage consumption, and all historical positions of the vehicle are stored in a mean trajectory map
We proposed a bathymetric PF Simultaneous localization and mapping (SLAM) method to reduce the computational consumption and identify invalid loop closures
Summary
Autonomous underwater vehicles (AUVs) have been used for a variety of tasks, including oceanographic surveys, demining, and seabed mappings [1]. Q. Zhang et al.: Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation a substantial amount of memory resources for storage is still needed to store the grid map. Contribution: In the proposed PF-BSLAM method, the whole trajectory of the vehicle is estimated in two parts: particles only keep the vehicle’s current estimate positions to reduce the storage consumption, and all historical positions of the vehicle are stored in a mean trajectory map. To detect the loop closure between a particle’s current estimated positions and historical positions of the vehicle, a temporary map is constructed in particles closed to possible overlapping areas use the inverse distance weighting (IDW) interpolation algorithm. Compared with the grid and trajectory map representations, the proposed method is more efficient and accurate because the use of submap matching allows more bathymetric observations to be matched in particle weighting.
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