ArticleSensors (Basel, Switzerland)2023
3D Object Detection for Self-Driving Cars Using Video and LiDAR: An Ablation Study.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Research on the Depth Image Reconstruction Algorithm Using the Two-Dimensional Kaniadakis Entropy Threshold.Sensors (Basel, Switzerland) · 2024Article
- An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
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Abstract
Methods based on 64-beam LiDAR can provide very precise 3D object detection. However, highly accurate LiDAR sensors are extremely costly: a 64-beam model can cost approximately USD 75,000. We previously proposed SLS-Fusion (sparse LiDAR and stereo fusion) to fuse low-cost four-beam LiDAR with stereo cameras that outperform most advanced stereo-LiDAR fusion methods. In this paper, and according to the number of LiDAR beams used, we analyzed how the stereo and LiDAR sensors contributed to the performance of the SLS-Fusion model for 3D object detection. Data coming from the stereo camera play a significant role in the fusion model. However, it is necessary to quantify this contribution and identify the variations in such a contribution with respect to the number of LiDAR beams used inside the model. Thus, to evaluate the roles of the parts of the SLS-Fusion network that represent LiDAR and stereo camera architectures, we propose dividing the model into two independent decoder networks. The results of this study show that-starting from four beams-increasing the number of LiDAR beams has no significant impact on the SLS-Fusion performance. The presented results can guide the design decisions by practitioners.
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