VIRUS-NeRF：視覚・赤外線・超音波を用いたNeural Radiance Fields
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields
低コストな超音波・赤外線測距センサとカメラを組み合わせ、占有グリッドを更新しながらNeRFで環境マップを構築する手法を提案。LiDARに匹敵するカバレッジを達成し、学習速度も46%向上した。
著者: Nicolaj Schmid, Cornelius von Einem, Cesar Cadena, Roland Siegwart, Lorenz Hruby, Florian Tschopp
分類: cs.RO, cs.CV, cs.LG, eess.SP
原文アブストラクト
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.
