融合激光与视觉数据，动态抗退化定位，提升自动驾驶复杂环境下的导航精度。
A2DO: Adaptive Anti-Degradation Odometry with Deep Multi-Sensor Fusion for Autonomous Navigation
- 通过深度网络融合激光与视觉信息，用注意力机制动态缓解传感器退化。
- 在模拟与真实数据上训练，多种恶劣条件下定位误差显著低于基线方法。
- 适合需要高鲁棒性定位的自动驾驶系统，尤其适用于光照差或天气恶劣场景。
精确的定位对自动驾驶车辆的安全有效导航至关重要，同时定位与地图构建（SLAM）是核心技术之一。然而，在低光、恶劣天气或遮挡等挑战性条件下，传感器退化会导致SLAM性能下降。本文提出A2DO，一种基于深度神经网络的端到端多传感器融合里程计系统，通过融合LiDAR与视觉数据，采用多层多尺度特征编码模块并引入注意力机制，实现对传感器退化的动态补偿。系统在涵盖广泛退化场景的仿真数据集上进行预训练，并在精选的真实数据集上微调，确保对复杂环境的强适应能力。实验表明，A2DO在多种退化条件下均保持优异的定位精度与鲁棒性，具备在自动驾驶系统中实际应用的潜力。
原文摘要 · Abstract (English)
Accurate localization is essential for the safe and effective navigation of autonomous vehicles, and Simultaneous Localization and Mapping (SLAM) is a cornerstone technology in this context. However, The performance of the SLAM system can deteriorate under challenging conditions such as low light, adverse weather, or obstructions due to sensor degradation. We present A2DO, a novel end-to-end multi-sensor fusion odometry system that enhances robustness in these scenarios through deep neural networks. A2DO integrates LiDAR and visual data, employing a multi-layer, multi-scale feature encoding module augmented by an attention mechanism to mitigate sensor degradation dynamically. The system is pre-trained extensively on simulated datasets covering a broad range of degradation scenarios and fine-tuned on a curated set of real-world data, ensuring robust adaptation to complex scenarios. Our experiments demonstrate that A2DO maintains superior localization accuracy and robustness across various degradation conditions, showcasing its potential for practical implementation in autonomous vehicle systems.
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