用神经网络端到端融合多传感器数据，无需手动调参即可提升自动驾驶定位精度。
An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization
- 设计端到端神经网络，自动编码传感器特征并融合
- 在真实场景中定位误差显著低于传统方法
- 适合追求高鲁棒性定位的自动驾驶系统开发者
多传感器融合对自动驾驶车辆定位至关重要，可整合多源数据以提高精度与可靠性。定位与姿态的准确性依赖于不确定性建模的精确性。传统方法通常假设不确定性服从高斯分布，并需人工调参，难以扩展且难以应对长尾场景。为此，我们提出一种基于学习的方法，利用高阶神经网络特征编码传感器信息，从而无需显式不确定性估计。该方法通过专门设计的端到端神经网络，大幅减少参数调优需求。实验表明，在真实自动驾驶场景中，该方法在准确性和鲁棒性上均优于现有融合方法。结果视频可访问：https://youtu.be/q4iuobMbjME。
原文摘要 · Abstract (English)
Multi-sensor fusion is essential for autonomous vehicle localization, as it is capable of integrating data from various sources for enhanced accuracy and reliability. The accuracy of the integrated location and orientation depends on the precision of the uncertainty modeling. Traditional methods of uncertainty modeling typically assume a Gaussian distribution and involve manual heuristic parameter tuning. However, these methods struggle to scale effectively and address long-tail scenarios. To address these challenges, we propose a learning-based method that encodes sensor information using higher-order neural network features, thereby eliminating the need for uncertainty estimation. This method significantly eliminates the need for parameter fine-tuning by developing an end-to-end neural network that is specifically designed for multi-sensor fusion. In our experiments, we demonstrate the effectiveness of our approach in real-world autonomous driving scenarios. Results show that the proposed method outperforms existing multi-sensor fusion methods in terms of both accuracy and robustness. A video of the results can be viewed at https://youtu.be/q4iuobMbjME.
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