A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments

Abstract

A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or geometric methods cannot filter out dynamic feature points well. Considering the problem of dynamic objects easily interfering with the localization accuracy of SLAM systems, this paper proposes a new monocular SLAM algorithm for use in dynamic environments. This improved algorithm combines semantic information and geometric methods to filter out dynamic feature points. Firstly, an adjusted Mask R-CNN removes prior highly dynamic objects. The remaining feature-point pairs are matched via the optical-flow method and a fundamental matrix is calculated using those matched feature-point pairs. Then, the environment’s actual dynamic feature points are filtered out using the polar geometric constraint. The improved system can effectively filter out the feature points of dynamic targets. Finally, our experimental results on the TUM RGB-D and Bonn RGB-D Dynamic datasets showed that the proposed method could improve the pose estimation accuracy of a SLAM system in a dynamic environment, especially in the case of high indoor dynamics. The performance effect was better than that of the existing ORB-SLAM2. It also had a higher running speed than DynaSLAM, which is a similar dynamic visual SLAM algorithm.

Mapping with Monocular Camera Sensor under Adversarial Illumination for Intelligent Vehicles

“…It also introduces an inertial sensor to minimize the scale variance of monocular-vision-only SLAM. Further improvements are also proposed with sensor adaptability, dynamic environment, online calibration, and pose-graph reuse [ 32 , 33 , 34 , 35 , 36 ].…”

Research on preprocessing algorithm of indoor map partitioning and global path planning based on FAST

“…The final experimental data are shown in Table 1. Analyzing Table 1, it is found that without preprocessing by the map partition algorithm, the average computing time required by the JPS algorithm for tasks (1,9) and (5,3) is 1.8825s and 2.5562s, respectively. However, after preprocessing by the map partition algorithm, the average calculation time of JPS in the same task was shortened to 1.4762s and 2.0639s.…”

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