In autonomous driving systems, LiDAR and radar play important roles in the
perception of the surrounding environment.LiDAR provides accurate 3D spatial
sensing information but cannot work in adverse weather like fog. On the other
hand, the radar signal can be diffracted when encountering raindrops or mist
particles thanks to its wavelength, but it suffers from large noise. Recent
state-of-the-art works reveal that fusion of radar and LiDAR can lead to robust
detection in adverse weather. The existing works adopt convolutional neural
network architecture to extract features from each sensor data stream, then
align and aggregate the two branch features to predict object detection
results. However, these methods have low accuracy of bounding box estimations
due to a simple design of label assignment and fusion strategies. In this
paper, we propose a bird's-eye view fusion learning-based anchor box-free
object detection system, which fuses the feature derived from the radar
range-azimuth heatmap and the LiDAR point cloud to estimate the possible
objects. Different label assignment strategies have been designed to facilitate
the consistency between the classification of foreground or background anchor
points and the corresponding bounding box regressions. In addition, the
performance of the proposed object detector is further enhanced by employing a
novel interactive transformer module. The superior performance of the proposed
methods in this paper has been demonstrated using the recently published Oxford
radar robotCar dataset, showing that the average precision of our system
significantly outperforms the best state-of-the-art method by 14.4% and 20.5%
at IoU equals 0.8 in clear and foggy weather testing, respectively.

该论文提出一种基于鸟瞰视角融合学习的无锚定框物体检测系统，融合雷达和光学雷达的特征来估计可能的目标，并采用新颖交互式变形模块来进一步提高性能。在最近发布的牛津雷达机器人车数据集上进行测试，该系统在 0.8 IoU 下的性能显着优于最佳现有技术方法，可在清晰和雾天下分别提高 14.4％和 20.5％的平均精度。