融合热成像与可见光图像，提升越野导航在昼夜变化下的可靠性
IRisPath: Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability
- 采用热成像与可见光图像的多模态融合策略，增强动态光照与天气下的感知鲁棒性
- 提出目标无关的相机外参标定方法，平移误差小于±1.7cm，旋转误差小于±0.827°
- 开源首个带伪标签的昼夜双模态越野数据集，支持可复现研究
自动驾驶越野导航在农业、建筑、搜救和国防等领域具有重要应用。传统道路自动驾驶方法难以应对动态地形，导致越野环境下车辆控制性能下降。近期深度学习模型虽结合感知传感器与运动反馈实现越野导航，但存在域外不确定性问题。光照与天气变化会显著影响模型表现。本文提出多模态融合网络IRisPath，利用热成像（IR）与可见光（RGB）图像，在昼夜及复杂气象条件下提升路径可通行性判断的鲁棒性。为推动该领域研究，我们还开源了一个包含昼夜热成像与可见光图像的越野数据集，并提供可通行性伪标签。此外，针对多传感器融合需求，开发了一种无需目标的热成像、激光雷达与可见光相机外参标定方法，实现平移精度±1.7cm、旋转精度±0.827°的高精度对齐。
原文摘要 · Abstract (English)
Autonomous off-road navigation is required for applications in agriculture, construction, search and rescue and defence. Traditional on-road autonomous methods struggle with dynamic terrains, leading to poor vehicle control in off-road conditions. Recent deep-learning models have used perception sensors along with kinesthetic feedback for navigation on such terrains. However, this approach has out-of-domain uncertainty. Factors like change in time of day and weather impacts the performance of the model. We propose a multi modal fusion network "IRisPath" capable of using Thermal and RGB images to provide robustness against dynamic weather and light conditions. To aid further works in this domain, we also open-source a day-night dataset with Thermal and RGB images along with pseudo-labels for traversability. In order to co-register for fusion model we also develop a novel method for targetless extrinsic calibration of Thermal, LiDAR and RGB cameras with translation accuracy of +/-1.7cm and rotation accuracy of +/-0.827degrees.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。