LoopSmart: Smart Visual SLAM Through Surface Loop Closure

TL;DR

LoopSmart introduces a novel visual SLAM framework that combines sparse feature matching with dense surface alignment, utilizing efficient CUDA-based registration and map-dependent verification to improve loop closure and surface reconstruction accuracy.

Contribution

The paper presents a new SLAM approach that integrates sparse and dense methods with innovative CUDA-based registration and verification techniques for enhanced loop closure.

Findings

Outperforms state-of-the-art in trajectory accuracy

Achieves superior surface reconstruction quality

Efficient CUDA implementation speeds up registration

Abstract

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. To achieve smart dense surface loop closure, a highly efficient CUDA-based global point cloud registration method and a map content dependent loop verification method are proposed. We run extensive experiments on different datasets, our method outperforms state-of-the-art ones in terms of both camera trajectory and surface reconstruction accuracy.