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.

翻译：视觉惯性里程计是移动机器人导航中的关键技术，传统方法依赖配备大量像素的相机。图像采集与处理需消耗大量计算资源。本文提出一种用于平面里程计的最小化方法，证明仅需四个视觉测量值和一个惯性测量单元即可为差速驱动机器人提供鲁棒的运动估计。我们的核心洞见在于：通过光学加伯掩模感知世界的四个向下光电二极管，其输出信号可编码速度信息。基于此，我们联合优化掩模参数与时间卷积网络，并利用物理仿真器进行训练。最终模型仅需光电二极管的四个测量值即可解码速度。将这些速度估计与IMU角速度相结合，可生成连续的平面运动轨迹。我们通过安装在差速驱动机器人上的原型传感器验证了该方法。在多种室内外地形中，系统无需任何真实世界微调即可紧密跟踪参考真值。本研究证明，最小化传感技术能够实现高效且精确的平面里程计。