Robust Multi-Camera BEV Perception: An Image-Perceptive Approach to Counter Imprecise Camera Calibration

Engineering notes

Key topics: autonomous driving system, autonomous driving, bev perception, bev, nuscenes, perception. 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

Recently, Bird’s Eye View (BEV) detection methodologies that utilize surround-view cameras have seen significant advancements in autonomous driving systems. Traditional methods, however, are constrained by their reliance on specific camera parameters, which poses challenges in generalizing across different vehicle-mounted cameras with varying poses and under adverse conditions. To address these challenges, we propose a robust BEV representation network that integrates Dual-Space Positional Encoding (DSPE) and image perception. This network is designed to enhance resilience to calibration errors and pose fluctuations, resulting in reliable detection performance on the Nuscenes dataset, even with imprecise extrinsic inputs. Our approach demonstrates competitive accuracy when compared to other methods that do not rely on temporal data, highlighting the effectiveness of our DSPE strategy in improving the robustness and accuracy of BEV detection in dynamic and challenging environments.

Links and sources

• Official / arXiv page

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