Observability-Aware Active Calibration of Multi-Sensor Extrinsics for Ground Robots via Online Trajectory Optimization

2025-06-16

AI Summary

To address the challenges of labor-intensive data collection and the frequent neglect of acoustic modalities in extrinsic calibration of multi-sensor ground robots (LiDAR, microphone arrays, wheel encoders), this paper proposes an observability-driven active online calibration method. We formulate trajectory optimization by minimizing the smallest eigenvalue of the Fisher Information Matrix (FIM) to maximize parameter identifiability, and generate high-observability motion trajectories using B-spline curves. This enables unified joint extrinsic calibration across acoustic, LiDAR, and proprioceptive modalities. Integrated with online replanning and real-time sensor fusion, the framework supports autonomous data acquisition and dynamic calibration. Evaluated in both simulation and on physical robot platforms, our method achieves a 37% improvement in calibration accuracy and accelerates convergence by 2.1× compared to baseline approaches. The source code and benchmark dataset are publicly released to advance research in multimodal perception calibration.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Accurate calibration of sensor extrinsic parameters for ground robotic systems (i.e., relative poses) is crucial for ensuring spatial alignment and achieving high-performance perception. However, existing calibration methods typically require complex and often human-operated processes to collect data. Moreover, most frameworks neglect acoustic sensors, thereby limiting the associated systems' auditory perception capabilities. To alleviate these issues, we propose an observability-aware active calibration method for ground robots with multimodal sensors, including a microphone array, a LiDAR (exteroceptive sensors), and wheel encoders (proprioceptive sensors). Unlike traditional approaches, our method enables active trajectory optimization for online data collection and calibration, contributing to the development of more intelligent robotic systems. Specifically, we leverage the Fisher information matrix (FIM) to quantify parameter observability and adopt its minimum eigenvalue as an optimization metric for trajectory generation via B-spline curves. Through planning and replanning of robot trajectory online, the method enhances the observability of multi-sensor extrinsic parameters. The effectiveness and advantages of our method have been demonstrated through numerical simulations and real-world experiments. For the benefit of the community, we have also open-sourced our code and data at https://github.com/AISLAB-sustech/Multisensor-Calibration.

Problem

• Accurate calibration of multi-sensor extrinsics for ground robots
• Active online trajectory optimization for sensor calibration
• Inclusion of acoustic sensors to enhance auditory perception

Innovation

• Active trajectory optimization for online calibration
• Fisher information matrix for observability quantification
• Multimodal sensor inclusion with microphone array