arXiv cs.AI· Sacha Jungerman, Kaustubh Sadekar, Avery Gump, Sriram Narayanan, Shantanu Gupta, Bhuyashi Deka, Srinivasa Narasimhan, Atul Ingle, Mohit Gupta·· 11 小时前AI 评分32
VisionSIM：面向机器人感知的多模态物理仿真平台
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
AI 导读
VisionSIM 是一个统一的多模态机器人感知仿真平台，对相机及 event、SPAD、iToF/dToF、热成像等新兴传感器建模原始物理测量过程，而非理想化处理。
正文 · 原文
Abstract: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.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.08368 [cs.RO] |
| (or arXiv:2611.08368v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08368 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sacha Jungerman [view email]
[v1]
Thu, 5 Nov 2026 17:48:00 UTC (28,007 KB)
来源：arXiv cs.AI · arxiv.org