Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots

2026-06-17

AI Summary

This study addresses the lack of systematic evaluation of multimodal SLAM performance under aggressive motions of legged robots, where sensor configuration critically influences robustness and accuracy. Leveraging the ANYmal D platform and the GrandTour dataset, this work presents the first quantitative analysis of visual, visual-inertial, and LiDAR-visual-inertial SLAM across varying camera modalities (monocular, stereo, RGB-D), shutter types (global vs. rolling), and IMU quality levels, assessing localization accuracy, robustness, and computational overhead. The findings reveal that stereo vision consistently outperforms monocular and RGB-D configurations; global shutters substantially mitigate motion-blur-induced tracking failures; and, notably, fusing low-grade IMUs can degrade the performance of otherwise capable visual pipelines in highly dynamic scenarios. These insights establish practical hardware design guidelines for perception systems on agile legged robots.

Abstract

Autonomous navigation of quadrupedal robots in diverse environments fundamentally relies on resilient Simultaneous Localization and Mapping (SLAM). While visual-inertial SLAM has matured across wheeled, handheld, and aerial platforms, a critical evaluation gap remains regarding how hardware-level sensor configurations affect performance under the aggressive dynamics of legged locomotion. Quadrupeds introduce distinct embodiment-induced sensory challenges, including foot-impact shocks, high-frequency mechanical vibrations, and rapid angular rotations, which degrade standard perception pipelines. To address this gap, we present a systematic evaluation of state-of-the-art visual, visual-inertial, and LiDAR-visual-inertial SLAM methods using the GrandTour dataset recorded on an ANYmal D quadruped. We isolate and quantify the impacts of camera modalities, shutter techniques, and inertial sensor tiers, analyzing their trade-offs across localization accuracy, algorithmic robustness, and computational resource utilization. Our empirical findings demonstrate that hardware selection has substantial influence on system resilience: stereo configurations consistently outperform monocular and RGB-D modalities, global shutter cameras significantly mitigate motion-induced tracking failures compared to rolling shutter cameras, and, crucially, standard inertial integration can degrade the performance of primarily vision-based frameworks under harsh legged locomotion. These insights additionally offer concrete design guidelines for tailoring custom sensor payloads to achieve dependable perception on agile legged systems.

Problem

• quadruped robots
• sensor configuration
• multimodal SLAM
• legged locomotion
• perception robustness

Innovation

• multimodal SLAM
• quadruped robots
• sensor configuration
• global shutter
• visual-inertial odometry