A2DO：面向自主导航的深度多传感器融合自适应抗退化里程计
A2DO: Adaptive Anti-Degradation Odometry with Deep Multi-Sensor Fusion for Autonomous Navigation
AI总结：
针对SLAM在传感器退化场景下性能下降的问题，提出端到端多传感器融合里程计A2DO，通过注意力机制动态缓解退化，经模拟预训练和真实微调，实验验证了其高精度与鲁棒性。
摘要：
精确的定位对于自主车辆的安全有效导航至关重要，而同步定位与建图（SLAM）是此背景下的基石技术。然而，在低光照、恶劣天气或传感器退化导致的遮挡等挑战性条件下，SLAM系统的性能可能会恶化。我们提出A2DO，一种新颖的端到端多传感器融合里程计系统，通过深度神经网络增强在这些场景下的鲁棒性。A2DO集成LiDAR和视觉数据，采用多层、多尺度特征编码模块，并辅以注意力机制，以动态缓解传感器退化。该系统在覆盖广泛退化场景的模拟数据集上进行了大量预训练，并在精选的真实世界数据上进行了微调，确保对复杂场景的稳健适应。我们的实验表明，A2DO在各种退化条件下均能保持卓越的定位精度和鲁棒性，展示了其在自主车辆系统中实际应用的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
