PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization

TL;DR

PROB-SLAM enhances real-time visual SLAM by integrating probabilistic semantic information, reducing uncertainty effects, and improving accuracy in dynamic and indoor environments through a novel Gaussian-based probability map.

Contribution

This paper introduces a probabilistic semantic map for SLAM that effectively manages semantic detection uncertainty, improving robustness and accuracy over traditional methods.

Findings

Improves ORB-SLAM2 accuracy by about 15% in indoor environments.

Effectively handles dynamic objects in SLAM scenarios.

Reduces impact of semantic detection uncertainty on optimization.

Abstract

Traditional SLAM algorithms are typically based on artificial features, which lack high-level information. By introducing semantic information, SLAM can own higher stability and robustness rather than purely hand-crafted features. However, the high uncertainty of semantic detection networks prohibits the practical functionality of high-level information. To solve the uncertainty property introduced by semantics, this paper proposed a novel probability map based on the Gaussian distribution assumption. This map transforms the semantic binary object detection into probability results, which help establish a probabilistic data association between artificial features and semantic info. Through our algorithm, the higher confidence will be given higher weights in each update step while the edge of the detection area will be endowed with lower confidence. Then the uncertainty is undermined and has less effect on nonlinear optimization. The experiments are carried out in the TUM RGBD dataset, results show that our system improves ORB-SLAM2 by about 15% in indoor environments' errors. We have demonstrated that the method can be successfully applied to environments containing dynamic objects.