论文/arXiv 2605.19990·19 天前

极简视觉惯性里程计

提出用四个带 Gabor 掩膜的向下光电二极管和 IMU，经仿真联合优化与 TCN 解码，在差速驱动机器人上验证平面里程计。

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原文链接：Minimalist Visual Inertial Odometry（arXiv 2605.19990 · 发布日期：2026-10-09）

Minimalist Visual Inertial Odometry

提出用四个带 Gabor 掩膜的向下光电二极管和 IMU，经仿真联合优化与 TCN 解码，在差速驱动机器人上验证平面里程计。

核心要点

• 传统 VIO 依赖大量像素，相机图像采集与处理资源开销较大。
• 四个经光学 Gabor 掩膜观测地面的光电二极管可产生编码速度的信号。
• 在物理仿真器中联合优化掩膜参数和 TCN，从四路测量解码速度。
• 原型传感器在差速驱动机器人室内外地形上，无实机微调仍贴近参考轨迹。

验证范围与局限：验证限于安装原型传感器的差速驱动机器人，在多种室内外地形上的平面运动。

英文摘要 (Abstract)

Visual-Inertial Odometry (VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, showing that just four visual sensors (pixels) and an IMU provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from the four photodiode measurements. Pairing these estimates with an IMU's angular speed yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference trajectories without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.

作者：Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar

来源

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1. arXiv 2605.19990：Minimalist Visual Inertial Odometry访问 2026-10-21