A Lightweight Neural Network for Loop Closure Detection in Indoor Visual SLAM

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...

Tree of Words for Visual Loop Closure Detection in Urban SLAM

This paper introduces vision based loop closure detection in Simultaneous Localisation And Mapping (SLAM) using Tree of Words. The loop closure performance in a complex urban environment is examined and an additional feature is suggested for safer matching. A SLAM ground experiment in an urban area is performed using Tree of Words, a delayed state information filter and planar laser scans for r...

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...

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...

Loop closure detection in SLAM by combining visual and spatial appearance