実世界のセンサ故障モードを再現するマルチモーダル知覚シミュレータ
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
カメラやイベント・SPAD・ToF・熱カメラなど多様なセンサの物理的測定過程を精密にモデル化し、実機で問題となる故障モードを再現できる知覚シミュレータVisionSIMを提案した。
詳しい要約
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著者: Sacha Jungerman, Kaustubh Sadekar, Avery Gump, Sriram Narayanan, Shantanu Gupta, Bhuyashi Deka, Srinivasa Narasimhan, Atul Ingle, Mohit Gupta
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Robotic perception increasingly relies on heterogeneous sensor suites combining cameras with emerging modalities (event, SPAD, iToF/dToF, and thermal). Yet existing simulators idealize these sensors, hiding critical modality-specific failure modes that limit real-world deployment. We present VisionSIM, a unified, physically grounded simulation platform that elevates sensor realism to a first-class concern by modeling the raw physical measurement process for diverse conventional and emerging modalities. We expose fine-grained control over physical sensor parameters, reproducing failure modes overlooked by general-purpose simulators: pileup and glare for LiDAR; phase wrapping, saturation, and motion artifacts for iToF; refractory drops for event cameras; and history-dependent emission for thermal cameras. This enables principled cross-modality comparison on identical scenes and trajectories. We release the platform and a large indoor dataset with synchronized raw measurements and rich ground-truth annotations.