Comparison of slip-compensated odometry for various skid-steer suspensions on different ground surfaces.

Localization systems for mobile robots often integrate various positioning methods, with wheel odometry commonly employedin predictive stages of position estimation algorithms. Reinforced odometry can elevate overall accuracy, robustness, andredundancy of a localization system. This study introduces a methodology and experiment comparing slip-compensatedodometry using four algorithms based on enhanced kinematic models of skid steer suspension. The primary objective isto extrapolate these methods to diverse skid-steer suspensions for enhanced adaptability and modular performance, whilethe secondary goal is to increase odometry robustness using low-cost methods. Through the evaluation of algorithmicperformance across 10 distinct surface types, characterized by diverse slipping conditions, we aimed to determine theiradaptability to different traction conditions. This paper introduces a slip compensation method for modular robotic platforms,comparing four odometry algorithms across 24 suspension configurations and 10 surfaces. It enhances odometry accuracyand slip compensation efficiency, focusing on reducing Mean Absolute Error (MAE) and demonstrating algorithm adaptability todifferent suspension types and surfaces. In this extended study, experiments were conducted involving 24 distinct suspensionconfigurations, all employing skid-steer mechanics. The rigorous testing entailed multiple repetitions of two different trajectoriesacross 10 varied ground surfaces, amounting to a total of 1576 trials.