# 极简视觉惯性里程计

> 原文链接：https://robotics.hellobike.com/papers/2026-10-09-2605-19990
> 频道：papers | 更新日期：2026-10-09 | 标签：arxiv, cs.RO
> 核心摘要：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。

# 极简视觉惯性里程计

::: v-pre
> 原文链接：[Minimalist Visual Inertial Odometry](https://arxiv.org/abs/2605.19990)（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
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## 原始参考来源 (Sources)

- [arXiv 2605.19990：Minimalist Visual Inertial Odometry](https://arxiv.org/abs/2605.19990)