极简视觉惯性里程计

摘要

视觉惯性里程计（VIO）对移动机器人导航至关重要，但通常使用具有大量像素的相机。采集和处理相机图像需要大量资源。本文提出了一种最小化平面里程计方法，证明仅需四个视觉测量值和一个IMU即可为差速驱动机器人提供鲁棒的运动估计。我们的关键发现是：四个朝下的光电二极管通过光学Gabor掩膜感知环境时，产生的信号能编码速度信息。基于此，我们利用物理仿真器联合优化掩膜参数与时序卷积网络（TCN）。最终模型仅从光电二极管产生的四个测量值中解码速度，并将这些估计值与IMU的角速度相结合，获得连续平面轨迹。我们通过在差速驱动机器人上安装原型传感器验证了该方法。在不同室内外地形下，该系统无需任何真实场景微调即可紧密追踪参考真值。本研究表明，最小化感知能够实现高效且精确的平面里程计。

English

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, demonstrating that just four visual measurements and an IMU can 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 just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU 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 ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.