基于深度学习的行星漫游车视觉系统
Deep Learning Aided Vision System for Planetary Rovers
AI总结：
本文提出一种结合实时感知与离线地形重建的行星漫游车视觉系统，采用CLAHE增强立体影像、YOLOv11n目标检测和神经网络估计距离，结合Depth Anything V2模型生成深度图并融合为密集点云，实测显示其在1-10米范围内中位深度误差为2.26厘米。
摘要：
本研究提出了一种用于行星漫游车的视觉系统，结合实时感知与离线地形重建。实时模块整合CLAHE增强的立体影像、基于YOLOv11n的目标检测以及神经网络估计物体距离。离线模块利用Depth Anything V2度量单目深度估计模型从捕获的图像生成深度图，并通过Open3D融合为密集点云。实时管道提供的现实世界距离估计为定性重建提供可靠的度量上下文。在Chandrayaan 3 NavCam立体影像上的评估，对比CAHV基准，显示神经网络在1至10米范围内中位深度误差为2.26厘米。目标检测模型在灰度月球场景中保持良好的精度召回率平衡。该架构提供了一种可扩展且计算高效的自主行星探索视觉解决方案。
英文摘要：
This study presents a vision system for planetary rovers, combining real-time perception with offline terrain reconstruction. The real-time module integrates CLAHE enhanced stereo imagery, YOLOv11n based object detection, and a neural network to estimate object distances. The offline module uses the Depth Anything V2 metric monocular depth estimation model to generate depth maps from captured images, which are fused into dense point clouds using Open3D. Real world distance estimates from the real time pipeline provide reliable metric context alongside the qualitative reconstructions. Evaluation on Chandrayaan 3 NavCam stereo imagery, benchmarked against a CAHV based utility, shows that the neural network achieves a median depth error of 2.26 cm within a 1 to 10 meter range. The object detection model maintains a balanced precision recall tradeoff on grayscale lunar scenes. This architecture offers a scalable, compute-efficient vision solution for autonomous planetary exploration.
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。
