PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization
This work addresses uncertainty in semantic SLAM for robotics and autonomous systems, representing an incremental improvement over existing methods.
The paper tackles the problem of high uncertainty in semantic detection networks for visual SLAM by proposing a probability map based on Gaussian distribution to transform semantic binary object detection into probabilistic results, improving ORB-SLAM2 by about 15% in indoor errors on the TUM RGBD dataset.
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.