Image-based Localization for Self-driving Vehicles Based on Online Network Adjustment in A Dynamic Scope

Engineering notes

During the real-time localization stage, we fine-tune the network regressor online through the training images in adjacent locations in the map, which can enhance the localization accuracy significantly. We demonstrate the superior performance of our method through experiments on benchmark dataset.

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

Image-based localization provides an alternative solution for camera pose estimation, which is a crucial component for self-driving vehicles. Localization for vehicles requires continuous feedback. We propose a solution that can accurately estimate the vehicle position and orientation. In this solution, we provide a complete pipeline for self-driving vehicles, including map building and camera pose estimation. We first design a convolutional neural network and train the localization system based on the entire global map. During the real-time localization stage, we fine-tune the network regressor online through the training images in adjacent locations in the map, which can enhance the localization accuracy significantly. Depending on the vehicle motion, we adjust the scope of local training images dynamically. We demonstrate the superior performance of our method through experiments on benchmark dataset.

Links and sources

• Official / arXiv page

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