Weighted triplet loss based on deep neural networks for loop closure detection in VSLAM

Smart living is an emerging technology that has attracted a lot of attention all around the world. As a key technology of smart space, which is the principal part of smart living, the SLAM system has effectively expanded the ability of space intelligent robots to explore unknown environments. Loop closure detection is an important part of SLAM system and plays a very important role in eliminating cumulative errors. The SLAM system without loop closure detection is degraded to an odometer. The state estimation solely relying on an odometer will be seriously deviated in the long-term and large-scale navigation and positioning. This paper proposes a metric learning method that uses deep neural networks for loop closure detection based on triplet loss. The map points obtained by metric learning are fused with all map points in the current keyframe, and the map points that do not meet the filtering conditions are eliminated. Based on the Batch Hard Triplet loss, the weighted triplet loss function avoids suboptimal convergence in the learning process by applying weighted value constraints. At the same time, considering that fixed boundary parameters cannot be well adapted to the diversity of scales between different samples, we use the semantic similarity of anchor samples and negative samples to redefine boundary parameters. Finally, a SLAM system based on metric learning is constructed, and the SLAM dataset TUM and KITTI are used to evaluate the proposed model's accuracy rate and recall rate. The scene features in this method are extracted automatically through neural networks instead of being artificially set. Finally, a high-precision closed-loop detection method based on weight adaptive triple loss is effectively realised through the closed-loop detection experiment. The minimum relative pose error is 0.00048 m, which is 15.8% less than that of the closed-loop detection algorithm based on the word bag model.