Visual Global Localization Based on Deep Neural Netwoks for Self-Driving Cars

Engineering notes

Key topics: self-driving car, self-driving. 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

In this work, we present a visual global localization system based on Deep Neural Networks (DNNs) for self-driving cars, named DeepVgl(Deep Visual Global Localization). In training mode, DeepVglis trained with images and associated poses from datasets built during the mapping process; and, in operating mode, DeepVglreceives images captured online and infers the global poses of the self-driving car. To assess the performance of DeepVgl,we carried out experiments using datasets collected by experimental self-driving cars on trips made over long periods of time, thus including significant changes in the environment, traffic volume and weather conditions, as well as different times of the day and seasons of the year. Experimental results show that DeepVglis able to correctly locate the self-driving car up to 75% of the time for 0.2 m of accuracy and 96% of the time for 5 m of accuracy.

Links and sources

• Official / arXiv page

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