arXiv cs.AI· Sacha Jungerman, Kaustubh Sadekar, Avery Gump, Sriram Narayanan, Shantanu Gupta, Bhuyashi Deka, Srinivasa Narasimhan, Atul Ingle, Mohit Gupta·· 4 小时前AI 评分43
VisionSIM：超越理想化传感器的机器人感知多模态仿真平台
VisionSIM: Beyond Idealized Sensors - A Multi-Modal Simulator for Robot Perception
AI 导读
arXiv 论文提出 VisionSIM，一个物理真实的统一多模态机器人感知仿真平台，对摄像头与事件相机、SPAD、iToF/dToF、热成像等新兴模态的原始物理测量过程建模。平台可细粒度控制传感器参数，复现 LiDAR 堆积与眩光、iToF 相位缠绕与运动伪影、事件相机不应期丢点等被通用仿真器忽略的失效模式，并开源平台和带同步原始测量与真值标注的室内数据集。
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