极简视觉惯性里程计
Minimalist Visual Inertial Odometry
AI总结：
本文提出了一种极简的平面里程计方法，通过四个视觉测量和一个IMU实现差分驱动机器人的鲁棒运动估计，展示了极简传感在高效准确平面里程计中的应用。
摘要：
视觉-惯性里程计（VIO）对于移动机器人导航至关重要，但使用高像素相机需要大量资源。本文提出了一种极简方法用于平面里程计，证明仅四个视觉测量和一个IMU即可为差分驱动机器人提供可靠的运动估计。我们的关键见解是四个向下 facing 的光电二极管通过光学Gabor掩码感知世界，产生编码速度的信号。基于此，我们利用物理基础模拟器联合优化掩码参数和时间卷积网络（TCN）。所得到的模型仅通过光电二极管产生的四个测量值解码速度。将这些估计与IMU提供的角速度结合，可以得到连续的平面轨迹。我们通过将原型传感器安装在差分驱动机器人上验证了我们的方法。在多样化的室内和室外地形上，我们的系统能够紧密跟踪参考真实地面，无需任何现实中的微调。我们的工作表明，极简传感能够实现高效且准确的平面里程计。
英文摘要：
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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