A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization

Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment

This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points,...

Loop closure detection in SLAM by combining visual and spatial appearance

An Active Visual Loop Closure Detection and Validation System for Topological SLAM

Loop closing is a vital component for mobile robot navigation without a priori information of the environment, since the mobile robot has to explore, build and at the same time maintain a globally consistent map. Moreover, it allows the mobile robot to recover from positional drifts due to errors associated with sensor measurements once loop closing is performed. In this paper, an active loop c...

Combining Visual and Spatial Appearance for Loop Closure Detection in SLAM

In this paper we describe a system for use on a mobile robot that detects potential loop closures using both the visual and spatial appearance of the local scene. Loop closing is the act of correctly asserting that a vehicle has returned to a previously visited location. It is an important component in the search to make SLAM (Simultaneous Localization and Mapping) the reliable technology it sh...

LoopSmart: Smart Visual SLAM Through Surface Loop Closure

We present a visual simultaneous localization and mapping (SLAM) framework of closing surface loops. It combines both sparse feature matching and dense surface alignment. Sparse feature matching is used for visual odometry and globally camera pose fine-tuning when dense loops are detected, while dense surface alignment is the way of closing large loops and solving surface mismatching problem. T...