Fusing LiDAR and Radar with Pillars Attention for 3D Object Detection

In recent years, LiDAR has emerged as one of the primary sensors for mobile robots, enabling accurate detection of 3D objects. On the other hand, 4D millimeter-wave Radar presents several advantages which can be a complementary for LiDAR, including an extended detection range, enhanced sensitivity to moving objects, and the ability to operate seamlessly in various weather conditions, making it a highly promising technology. To leverage the strengths of both sensors, this paper proposes a novel fusion method that combines LiDAR and 4D millimeter-wave Radar for 3D object detection. The proposed approach begins with an efficient multi-modal feature extraction technique utilizing a pillar representation. This method captures comprehensive information from both LiDAR and millimeter-wave Radar data, facilitating a holistic understanding of the environment. Furthermore, a Pillar Attention Fusion (PAF) module is employed to merge the extracted features, enabling seamless integration and fusion of information from both sensors. This fusion process results in lightweight detection headers capable of accurately predicting object boxes. To evaluate the effectiveness of our proposed approach, extensive experiments were conducted on the VoD dataset. The experimental results demonstrate the superiority of our fusion method, showcasing improved performance in terms of detection accuracy and robustness across different environmental conditions. The fusion of LiDAR and 4D millimeter-wave Radar holds significant potential for enhancing the capabilities of mobile robots in real-world scenarios. The proposed method, with its efficient multi-modal feature extraction and attention-based fusion, provides a reliable and effective solution for 3D object detection.