Underwater Loop-Closure Detection for Mechanical Scanning Imaging Sonar by Filtering the Similarity Matrix With Probability Hypothesis Density Filter

Abstract

Robust and accurate estimation of position and attitude of a UUV (Unmanned Underwater Vehicle) from sonar scans is essential for simultaneous localization and mapping (SLAM). Both deadreckoning based on the inertial navigation system and the motion parameter estimation based on the registration of the ultrasound scan sequence can contribute to the performance of the system. However, the rapidly-growing accumulated error tends to counteract the precise localization of the vehicle. In this paper, a method for loop-closure detection is proposed that adjusts the accumulated error for the underwater acoustic SLAM when the vehicle scans the underwater environment using an Mechanical Scanning Imaging Sonar (MSIS). Firstly, a similarity matrix for pairs of scans is constructed to represent the loop-closing tracks. In the registration step, two novel features, namely the intensity projection histograms and a polar gradient matrix, are extracted to calculate the translational and rotational parameters respectively. Secondly, the probability hypothesis density (PHD) filter is used to extract the possible loop-closure constraints from the similarity matrix, removing the random noise brought by accidental correlation and refining the concurrent loop-closing tracks resulted from long-range scanning. Lastly, the loop-closure constraints from the refined loop-closing tracks are fed into the GraphSLAM system to adjust the pose of each scan by constraint optimization. Experiments on the MSIS sonar images collected in structured and unstructured underwater environments validate the effectiveness of the proposed loop-closure detection method. INDEX TERMS Forward-looking sonar, intensity projection histogram, PHD filter, polar gradient matrix, underwater loop-closure detection.

Synthetic Scan Formation for Underwater Mapping With Low-Cost Mechanical Scanning Sonars (MSS)

“…But this is also the strong point of SSF. It operates so to say on a level below conventional SLAM by optimizing the data that serves as input for the approaches according to the state of the art [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. As demonstrated in the experiments and field trials, this leads to improved results and the increase in time is minor; the synthetic scan formation is even usable for real-time mapping during a mission.…”

“…But when an MSS is mounted on an Unmanned Underwater Vehicle (UUV), there is the need for motion compensation to avoid distorted scans. The state of the art is to generate the motion-compensated scans in fixed time-windows, e.g., whenever the sensor has completed a full rotation between minimum and maximum angles, using the vehicle's navigation [5], [6], [7], [8], [9], [10], [11], [12], [13], VOLUME 11, 2023 [14], [15], [16], [17], [18].…”

Online appearance-based place recognition and mapping algorithms for autonomous robot navigation

“…Then, a similarity threshold determines if a location can be considered as loop-closure or should be declined, while additional steps, such as consistency checks based on multi-view geometry [22], can verify the matching pair. With an increasing demand for autonomous systems in a broad spectrum of applications, e.g., search and rescue [23][24][25], space [26,27] and underwater explorations [28][29][30][31], the robots need to operate precisely for an extended period. As their complexity is at least linear to the traversed path, this limitation constitutes a crucial factor, severely affecting their In recent years, loop-closure detection algorithms have matured enough to support continually enlarging operational environments.…”