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

• URL: http://arxiv.org/abs/2606.19067v1
• Date: Wed, 17 Jun 2026 13:41:07 GMT
• Title: Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots
• Abstract summary: We present a systematic evaluation of state-of-the-art visual, visual-inertial, and LiDAR-visual-inertial SLAM methods.<n>We 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.
• Score: 15.924751226144979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration [50.18488308004802]
  Sen-Cap is a 3D motion capture framework that integrates multi-modal data from LiDAR and camera.<n>Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion.<n>This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios.
  arXiv  Detail & Related papers  (2026-08-03T14:16:18Z)
• Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
  Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
  arXiv  Detail & Related papers  (2026-04-27T04:36:41Z)
• ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment [8.10554738370527]
  We propose a method that leverages deep learning to robustly track visual features in monocular camera images.<n>This method operates reliably even in textureless environments and situations with rapid lighting changes.<n>We evaluate the performance of our proposed method by integrating it into VINS-Fusion, a commonly used Visual-Inertial Odometry (VIO) system.
  arXiv  Detail & Related papers  (2026-03-19T10:49:22Z)
• Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera [54.967647497048205]
  We present Stereo-Inertial Poser, a real-time motion capture system that estimates metric-accurate and shape-aware 3D human motion.<n>We replace the monocular RGB with stereo vision, enabling direct 3D keypoint extraction and body shape parameter estimation.<n>Our method produces drift-free global translation under a long recording time and reduces foot-skating effects.
  arXiv  Detail & Related papers  (2026-03-02T17:46:38Z)
• ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments [6.718801307135799]
  We introduce a framework that unifies sensing, learning, and actuation into a closed reconfiguration loop.<n>We implement and evaluate this framework on a mobile rover, showing that adaptive control reduces load by 29.3% with only a 5.3% accuracy drop compared to a baseline.
  arXiv  Detail & Related papers  (2026-02-11T05:39:24Z)
• A2VISR: An Active and Adaptive Ground-Aerial Localization System Using Visual Inertial and Single-Range Fusion [17.59898575353575]
  It's a practical approach using the ground-aerial collaborative system to enhance the localization robustness of flying robots in cluttered environments.<n>We improve the ground-aerial localization framework in a more comprehensive manner, which integrates active vision, single-ranging, inertialometry, and optical flow.<n>The proposed approach achieves robust online localization, with an average root mean square error of approximately 0.09 m, while maintaining resilience to capture loss and sensor failures.
  arXiv  Detail & Related papers  (2025-12-18T10:07:06Z)
• Benchmarking Egocentric Visual-Inertial SLAM at City Scale [50.1245744173948]
  This paper introduces a new dataset and benchmark for visual-inertial SLAM with egocentric, multi-modal data.<n>We record hours and kilometers of trajectories through a city center with glasses-like devices equipped with various sensors.<n>We show that state-of-the-art systems developed by academia are not robust to these challenges and we identify components that are responsible for this.
  arXiv  Detail & Related papers  (2025-09-30T17:59:31Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception [4.9554369813645325]
  Perception systems in robotics encounter significant challenges in high-speed and dynamic conditions when relying on traditional cameras.<n>Brain-inspired vision sensors (BVS) have recently gained attention as an alternative, offering high temporal resolution with reduced bandwidth and power.<n>Here, we present the first quantitative evaluation framework for two representative classes of BVSs in variable-speed robotic sensing.
  arXiv  Detail & Related papers  (2025-04-27T14:24:06Z)
• Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor [58.305341034419136]
  We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
  arXiv  Detail & Related papers  (2023-08-28T07:56:13Z)
• VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots [5.789654849162465]
  We present visual inertial lidar legged navigation system (VILENS) for legged robots. The key novelty is the tight fusion of four different sensor modalities to achieve reliable operation. We show an average improvement of 62% translational and 51% rotational errors compared to a state-of-the-art loosely coupled approach.
  arXiv  Detail & Related papers  (2021-07-15T11:05:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.