用多视角位姿优化提升视觉惯性导航效率
A Multi-view Landmark Representation Approach with Application to GNSS-Visual-Inertial Odometry
- 将地标表示与多个相机位姿直接关联，实现位姿独占的测量模型
- 在仿真与实测中均显著降低计算负担并提升定位精度
- 适合需要实时性与高精度的多传感器融合系统应用
不变扩展卡尔曼滤波（IEKF）是视觉辅助传感器融合中的关键技术，但在联合优化相机位姿与特征点时常面临高计算开销。为此，本文提出一种多视角位姿独占估计方法，并应用于基于GNSS-视觉-惯性里程计（GVIO）的系统中。核心贡献在于构建了一种视觉观测模型，可直接将地标表示与多个相机位姿及观测值关联。该位姿独占测量模型被证明在地标与位姿间具有紧密耦合性，且保持与估计位姿无关的完美零空间特性。进一步地，将该方法集成至基于滤波器的GVIO系统，并引入新型特征管理策略。通过仿真测试与真实场景实验验证，所提方法在计算效率和定位精度方面均表现更优。
原文摘要 · Abstract (English)
Invariant Extended Kalman Filter (IEKF) has been a significant technique in vision-aided sensor fusion. However, it usually suffers from high computational burden when jointly optimizing camera poses and the landmarks. To improve its efficiency and applicability for multi-sensor fusion, we present a multi-view pose-only estimation approach with its application to GNSS-Visual-Inertial Odometry (GVIO) in this paper. Our main contribution is deriving a visual measurement model which directly associates landmark representation with multiple camera poses and observations. Such a pose-only measurement is proven to be tightly-coupled between landmarks and poses, and maintain a perfect null space that is independent of estimated poses. Finally, we apply the proposed approach to a filter based GVIO with a novel feature management strategy. Both simulation tests and real-world experiments are conducted to demonstrate the superiority of the proposed method in terms of efficiency and accuracy.
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