VIRUS-NeRF——基于视觉、红外与超声的神经辐射场
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields
AI总结：
针对移动机器人传统建图依赖昂贵传感器的问题，提出融合视觉、红外与超声数据的VIRUS-NeRF，基于Instant-NGP优化占用栅格，实现低成本下接近LiDAR的建图效果，训练速度提升46%。
摘要：
自主移动机器人日益成为现代工厂与仓储运营中不可或缺的组成部分。障碍物检测、避障与路径规划是与安全相关的关键任务，这类任务通常依赖昂贵的LiDAR传感器与深度相机来完成。我们提出通过开发VIRUS-NeRF（基于视觉、红外与超声的神经辐射场），采用低成本的低分辨率测距传感器，例如超声波与红外飞行时间传感器。VIRUS-NeRF以带多分辨率哈希编码的即时神经图形基元（Instant Neural Graphics Primitives with a Multiresolution Hash Encoding, Instant-NGP）为基础，融合超声波与红外传感器的深度测量值，并利用这些测量值更新光线步进所使用的占用栅格。二维环境下的实验评估表明，VIRUS-NeRF在覆盖率方面的建图性能与LiDAR点云相当。值得注意的是，在小型环境中，其精度与LiDAR测量结果一致；而在大型环境中，其性能受所用超声波传感器的限制。深入的消融研究显示，在处理稀疏数据与视角变化较少的场景时，添加超声波与红外传感器效果显著。此外，与Instant-NGP相比，VIRUS-NeRF提出的占用栅格提升了建图能力，并将训练速度提高了46%。总体而言，VIRUS-NeRF为移动机器人领域的低成本局部建图提供了一种颇具前景的方法，在安全与导航任务中具有应用潜力。代码可访问https://github.com/ethz-asl/virus_nerf获取。
英文摘要：
Autonomous mobile robots are an increasingly integral part of modern factory and warehouse operations. Obstacle detection, avoidance and path planning are critical safety-relevant tasks, which are often solved using expensive LiDAR sensors and depth cameras. We propose to use cost-effective low-resolution ranging sensors, such as ultrasonic and infrared time-of-flight sensors by developing VIRUS-NeRF - Vision, InfraRed, and UltraSonic based Neural Radiance Fields. Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching. Experimental evaluation in 2D demonstrates that VIRUS-NeRF achieves comparable mapping performance to LiDAR point clouds regarding coverage. Notably, in small environments, its accuracy aligns with that of LiDAR measurements, while in larger ones, it is bounded by the utilized ultrasonic sensors. An in-depth ablation study reveals that adding ultrasonic and infrared sensors is highly effective when dealing with sparse data and low view variation. Further, the proposed occupancy grid of VIRUS-NeRF improves the mapping capabilities and increases the training speed by 46% compared to Instant-NGP. Overall, VIRUS-NeRF presents a promising approach for cost-effective local mapping in mobile robotics, with potential applications in safety and navigation tasks. The code can be found at https://github.com/ethz-asl/virus nerf.
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。
