  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.
