DOE-SLAM: Dynamic Object Enhanced Visual SLAM

Sensors

Xiao Hu¹  Jochen Lang¹ 

[1] Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada;

关键词: vSLAM;    AR;    computer vision;   

DOI  :  10.3390/s21093091

来源: DOAJ

【 摘 要 】

In this paper, we formulate a novel strategy to adapt monocular-vision-based simultaneous localization and mapping (vSLAM) to dynamic environments. When enough background features can be captured, our system not only tracks the camera trajectory based on static background features but also estimates the foreground object motion from object features. In cases when a moving object obstructs too many background features for successful camera tracking from the background, our system can exploit the features from the object and the prediction of the object motion to estimate the camera pose. We use various synthetic and real-world test scenarios and the well-known TUM sequences to evaluate the capabilities of our system. The experiments show that we achieve higher pose estimation accuracy and robustness over state-of-the-art monocular vSLAM systems.

【 授权许可】

Unknown