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.

翻译：自主移动机器人正日益成为现代化工厂和仓库运营中不可或缺的组成部分。障碍物检测、避障与路径规划是至关重要的安全任务，通常需借助昂贵的激光雷达传感器和深度相机完成。我们提出利用低成本低分辨率测距传感器（如超声波和红外飞行时间传感器），通过开发VIRUS-NeRF——基于视觉、红外与超声波的神经辐射场来实现。VIRUS-NeRF以基于多分辨率哈希编码的即时神经图形基元（Instant-NGP）为基础，融合了超声波与红外传感器的深度测量值，并将其用于更新光线行进所需的占用网格。二维实验评估表明，VIRUS-NeRF在覆盖范围方面达到了与激光雷达点云相当的建图性能。值得注意的是，在小型环境中，其精度与激光雷达测量结果一致；而在大型环境中，精度受限于所使用的超声波传感器。深入的消融研究揭示，当处理稀疏数据和低视角变化时，添加超声波与红外传感器效果显著。此外，与Instant-NGP相比，VIRUS-NeRF提出的占用网格提升了建图能力，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有前景的方法，在安全和导航任务中具有潜在应用价值。代码详见 https://github.com/ethz-asl/virus nerf。