Researches advanced in VSLAM for dynamic environment

Abstract

Abstract Simultaneous Localization and Mapping (SLAM), which has been widely utilized in a number of different fields, including unmanned vehicle, path planning, and robotics, has always been a topic of significant concern to computer vision community. Visual SLAM (VSLAM) only relies on cameras as sensors. Compared with lidar SLAM, data collection is convenient and low-cost, and it is the most representative technical direction in SLAM research. Traditional visual SLAM (VSLAM) mainly focuses on the static or low speed objects. However, in real life, most objects are moving, which makes the performance of traditional visual SLAM technology in the dynamic environment is not ideal. Benefited from the development of deep learning, VSLAM in dynamic scenes has make breakthrough in both accuracy and robustness. In this paper, the concept of SLAM and the development history of VSLAM are briefly introduced, and then the common methods used for dynamic region detection in semantic VSLAM are described in detail, which mainly include methods that use deep learning, methods that use optical/scene flow, and methods that use multi-view geometry. In addition, the existing data sets and evaluation indexes are also introduced. At the end of the paper, the current problems and shortcomings of semantic VSLAM are pointed out, and the future is prospected.