Artificial Feature Generation and Optimal IMU Placement for VIO in Pipeline Inspection Robots

Abstract

To overcome the severe perceptual sparsity of pipeline interiors, this paper presents an active Visual-Inertial Odometry (VIO) framework that generates its own visual landmarks on the fly. By utilizing a pulse of compressed air through a modified airbrush, the robotic platform deposits non-uniform, non-permanent fluorescent markings onto the pipe walls, creating reliable visual features for a dual-camera front-end. Alongside this active perception strategy, the system employs a multi-IMU sensor suite optimized specifically for the cylindrical manifold of pipe environments. We demonstrate via Fisher Information analysis that a closed-form, optimally placed IMU configuration significantly enhances the observability of the robot’s motion, proving that geometric arrangement dominates over mere sensor count. Validated across diverse pipe geometries and surface textures in both simulation and real-world experiments, the proposed VIO approach consistently eliminates tracking failures, achieves lower trajectory RMSE than visual-only and sub-optimal inertial baselines, and keeps the residual errors strictly contained within the pipe corridor.