System-Level Evaluation of Approximate Analog Vision Front-Ends for Indoor Robot Navigation

Indoor robots continuously capture and process camera images, heavily taxing battery life and bandwidth. While approximate analog computing offers massive power savings by deliberately degrading sensor precision, its impact on closed-loop robotic autonomy remains largely unexplored. In this work, we introduce the first system-level evaluation framework explicitly linking analog circuit-level imperfections—including low-bit quantization, read noise, and dynamic-range clipping—with downstream 3D navigation. Through 10,500 end-to-end planning evaluations combined with 1,996 geometric mapping evaluations using diverse perception and mapping algorithms, we uncover a severe non-linear "error cascade" across the software stack. Crucially, we identify a fundamental perception paradigm shift: while semantic free-space segmentation exhibits extreme fault tolerance down to 4-bit precision, geometric perception tasks have task-specific minimum precision requirements: visual odometry remains viable at 6-bit, while monocular depth estimation acts as the system’s tightest constraint, demanding a full 8-bit baseline. Furthermore, our energy-quality Pareto analysis reveals a counter-intuitive anomaly: deliberately applying an aggressive 0.8V dynamic range clipping acts as an analog-domain noise filter, which, when paired with TSDF mapping and Theta* planning, simultaneously reduces front-end energy and increases the overall navigation success rate from 61% to 64%. Ultimately, this work provides actionable quantitative guidelines for interdisciplinary hardware-algorithm co-design in next-generation edge robotics.