Autonomous driving paper index
Monocular Depth Estimation Applied to Global Localization Over 2D Floor Plans Using Free Space Density
One-line summary
In this paper, we propose a monocular camera-based localization of a motorized wheeled robot using a 2D floor plan as a reference map.
Engineering notes
Key topics: autonomous driving, depth estimation, monocular depth, monocular camera. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Indoor global localization is a critical aspect of autonomous robotic navigation. The increasing demand for service consumer-grade robots that require self-localization calls for research on methods that work with easy setup and low-cost sensors. In this paper, we propose a monocular camera-based localization of a motorized wheeled robot using a 2D floor plan as a reference map. The innovation of our method lies in using depth maps estimated from monocular images to compute the free space around the robot to be used as a measurement model in a particle filter strategy. The estimated free space density is compared to the free space density extracted from particles in the 2D floor plan. Due to the inherent imperfections of estimated depth maps, we also propose a new particle weighting approach to account for uncertainties in the depth estimation from the monocular camera. Experiments performed using real-world scenario sequences of images comparing the proposed method with RGB-D camera-based approaches demonstrate the effectiveness of the method, even for imperfect depth maps obtained with the monocular depth estimation model.
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