HiRo-SLAM is a visual-inertial SLAM system developed to achieve high accuracy and enhanced robustness. To address critical limitations of conventional methods, including systematic biases from imperfect camera models, uneven spatial feature distribution, and the impact of outliers, we propose a unified optimization framework that integrates four key innovations. First, Precise Camera Projection Modeling (PCPM) embeds a fully differentiable camera model in nonlinear optimization, ensuring accurate handling of camera intrinsics and distortion to prevent error accumulation. Second, Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) quantifies feature point contribution through a multi-scale pyramid, providing uniform visual constraints in weakly textured or repetitive regions. Third, Robust Optimization Using Graduated Non-Convexity (GNC) suppresses outliers through dynamic weighting, preventing convergence to local minima. Finally, the Point-Line Feature Fusion Frontend combines XFeat point features with SOLD2 line features, leveraging multiple geometric primitives to improve perception in challenging environments, such as those with weak textures or repetitive structures. Comprehensive evaluations on the EuRoC MAV, TUM-VI, and OIVIO benchmarks show that HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods. On the EuRoC MAV dataset, HiRo-SLAM achieves a 30.0% reduction in absolute trajectory error compared to strong baselines and attains millimeter-level accuracy on specific sequences under controlled conditions. However, while HiRo-SLAM demonstrates state-of-the-art performance in scenarios with moderate texture and minimal motion blur, its effectiveness may be reduced in highly dynamic environments with severe motion blur or extreme lighting conditions.
Deng et al. (2026) studied this question.