MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion

TL;DR

MoCap2GT is a novel joint optimization method that fuses MoCap and IMU data to produce highly accurate ground truth trajectories for SLAM benchmarking, addressing calibration errors and jitter issues.

Contribution

It introduces a robust, higher-order B-spline pose parameterization with variable time offset and a degeneracy-aware rejection strategy for improved GT accuracy.

Findings

Outperforms existing methods in accuracy

Provides precise rotation and translation error assessment

Enhances SLAM benchmarking reliability

Abstract

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).