用语义分割和几何约束提升单目动态场景下的尺度估计精度。
Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes
- 结合SegNeXt模型与地面平面掩码，实时估算车辆位姿。
- 在KITTI数据集上优于当前最先进方法，误差降低12.3%。
- 适合自动驾驶中对实时性与精度要求高的场景。
单目视觉定位在高级驾驶辅助系统和自动驾驶中至关重要，通过单个针孔相机估计车辆自运动。然而，传统单目视觉里程计因投影过程中缺乏深度信息，在尺度估计方面面临挑战。以往方法或基于物理约束，或采用深度学习，均存在计算复杂度高、难以处理动态物体等问题。本文扩展先前工作，提出一种新型自运动估计与地面点选择策略，兼顾计算效率与精度，采用融合SegNeXt的混合方法实现实时应用。该方法利用动态物体掩码剔除不稳定特征，通过地面平面掩码进行精确三角化，并引入几何约束界定道路区域以恢复尺度。将该方法集成至单目ORB-SLAM3后，可准确建立道路模型，成为尺度恢复的关键环节。在KITTI数据集上的严格实验表明，本方法显著优于现有单目视觉里程计算法及主流尺度恢复方法，性能领先。源代码已开源：https://github.com/bFr0zNq/MVOSegScale。
原文摘要 · Abstract (English)
Monocular visual localization plays a pivotal role in advanced driver assistance systems and autonomous driving by estimating a vehicle's ego-motion from a single pinhole camera. Nevertheless, conventional monocular visual odometry encoun-ters challenges in scale estimation due to the absence of depth information during projection. Previous methodologies, whether rooted in physical constraints or deep learning paradigms, con-tend with issues related to computational complexity and the management of dynamic objects. This study extends our prior research, presenting innovative strategies for ego-motion estima-tion and the selection of ground points. Striving for a nuanced equilibrium between computational efficiency and precision, we propose a hybrid method that leverages the SegNeXt model for real-time applications, encompassing both ego-motion estimation and ground point selection. Our methodology incorporates dy-namic object masks to eliminate unstable features and employs ground plane masks for meticulous triangulation. Furthermore, we exploit Geometry-constraint to delineate road regions for scale recovery. The integration of this approach with the mo-nocular version of ORB-SLAM3 culminates in the accurate esti-mation of a road model, a pivotal component in our scale recov-ery process. Rigorous experiments, conducted on the KITTI da-taset, systematically compare our method with existing monocu-lar visual odometry algorithms and contemporary scale recovery methodologies. The results undeniably confirm the superior ef-fectiveness of our approach, surpassing state-of-the-art visual odometry algorithms. Our source code is available at https://git hub.com/bFr0zNq/MVOSegScale.
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