EMaxLoc: Scalable Vision Transformer-Based Camera Relocalization for Large-Scale Driving Environments

Engineering notes

Extensive experiments validate EMaxLoc’s superior performance with increasing dataset sizes, surpassing existing deep learning-based methods such as AtLoc, PoseNet, MapNet, and EffLoc in large-scale driving environments.

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

Large-scale camera localization in driving environments is pivotal in autonomous driving and robotics, where deep learning-based solutions offer promise for end-to-end problem-solving. However, existing learning-based camera pose regression methods often encounter accuracy and robustness challenges in complex environments, hampering scalability to large scenarios and datasets. This work introduces EMaxLoc, a novel end-to-end camera pose regression model that integrates a multiaxis Vision Transformer (MaxViT) with an efficient multiscale attention (EMA) module for large-scale localization. The introduced MaxViT extractor captures both local and global spatial features through its block and grid attention mechanisms, while the EMA module enhances multiscale feature fusion and channelwise contextual modeling. EMaxLoc demonstrates remarkable proficiency in environments characterized by scene dynamics and illumination changes. Furthermore, our Transformer-based framework showcases scalability, suggesting potential for continual improvement in localization performance. Extensive experiments validate EMaxLoc’s superior performance with increasing dataset sizes, surpassing existing deep learning-based methods such as AtLoc, PoseNet, MapNet, and EffLoc in large-scale driving environments.

Links and sources

• Official / arXiv page

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