Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data
This work addresses robustness issues in sensor fusion for autonomous driving, making it more applicable to real-world scenarios with corrupted data, though it appears incremental as it builds on existing fusion methods.
The paper tackles the problem of sensor fusion for 3D object detection in autonomous driving by addressing data corruptions such as misalignment and missing LiDAR data, resulting in a method that performs on par with state-of-the-art on normal data and outperforms them on misaligned data.
Multimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field. Nevertheless, most of these methods heavily rely on dense LiDAR data and accurately calibrated sensors which is often not the case in real-world scenarios. Data from LiDAR and cameras often come misaligned due to the miscalibration, decalibration, or different frequencies of the sensors. Additionally, some parts of the LiDAR data may be occluded and parts of the data may be missing due to hardware malfunction or weather conditions. This work presents a novel fusion step that addresses data corruptions and makes sensor fusion for 3D object detection more robust. Through extensive experiments, we demonstrate that our method performs on par with state-of-the-art approaches on normal data and outperforms them on misaligned data.