仅用4个像素和IMU实现高效精准的平面运动估计
Minimalist Visual Inertial Odometry
- 用四个向下视角光电二极管加光学戈勃滤镜感知速度信号
- 在多种室内外地形上无需调参即可精确跟踪参考轨迹
- 适合资源受限的差速移动机器人，部署简单且能耗低
视觉惯性里程计（VIO）对移动机器人导航至关重要，但传统方法依赖高像素摄像头，消耗大量计算资源。本文提出一种极简平面里程计方案，仅需四个视觉传感器（像素）与一个惯性测量单元（IMU），即可实现对差速驱动机器人的鲁棒运动估计。核心思路是：四个向下朝向的光电二极管通过光学戈勃（Gabor）掩膜感知环境，其输出信号编码了运动速度。基于物理驱动的仿真器，联合优化掩膜参数与时间卷积网络（TCN），使模型能从四路光电二极管信号中解码出速度。将该速度估计与IMU提供的角速度融合，即可获得连续的平面轨迹。我们在差速机器人上搭建原型系统进行验证，在多样化的室内外环境中，系统无需任何真实世界微调即能紧密追踪参考轨迹。结果表明，极简传感可实现高效且准确的平面里程计。
原文摘要 · Abstract (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, 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.
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