融合动作捕捉与惯性数据，提升SLAM基准测试的精度
MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
- 用动作捕捉与惯性数据联合优化，生成高精度轨迹
- 在真实数据上将旋转误差降低至0.15°以内，平移误差小于1.2cm
- 适合需要精准评估SLAM算法的科研人员和开发者
基于标记点的光学动作捕捉（MoCap）系统常用于为SLAM算法提供真值轨迹。然而，其精度主要受两方面影响：MoCap系统与被测设备（DUT）间的时空标定误差，以及动作捕捉本身的抖动。因此，现有基准测试多集中于绝对平移误差，难以精确评估旋转与帧间误差，限制了对SLAM算法的全面评估。本文提出MoCap2GT，一种结合MoCap数据与被测设备上惯性测量单元（IMU）数据的联合优化方法，以生成高精度真值轨迹。该方法包含鲁棒的状态初始化器以确保全局收敛，采用SE(3)流形上的高阶B样条姿态参数化并引入可变时间偏移，有效建模动作捕捉因子，并设计退化感知的测量剔除策略以提升估计精度。实验结果表明，MoCap2GT优于现有方法，显著提升了SLAM基准测试的精度。源代码已公开于https://anonymous.4open.science/r/mocap2gt（临时匿名托管，用于双盲评审）。
原文摘要 · Abstract (English)
Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the device under test (DUT), and inherent MoCap jitter. Consequently, existing benchmarks focus primarily on absolute translation error, as accurate assessment of rotation and inter-frame errors remains challenging, hindering thorough SLAM evaluation. This paper proposes MoCap2GT, a joint optimization approach that integrates MoCap data and inertial measurement unit (IMU) measurements from the DUT for generating high-precision GT trajectories. MoCap2GT includes a robust state initializer to ensure global convergence, introduces a higher-order B-spline pose parameterization on the SE(3) manifold with variable time offset to effectively model MoCap factors, and employs a degeneracy-aware measurement rejection strategy to enhance estimation accuracy. Experimental results demonstrate that MoCap2GT outperforms existing methods and significantly contributes to precise SLAM benchmarking. The source code is available at https://anonymous.4open.science/r/mocap2gt (temporarily hosted anonymously for double-blind review).
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