一种使用对象级运动估计和基于交叉视差的几何滤波的立体视觉SLAM系统
A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity
AI总结：
提出OCD SLAM框架，通过交叉视差几何方法检测动态特征点，并结合3D目标检测与卡尔曼滤波实现对象级运动分类，在KITTI数据集上显著提升轨迹精度。
摘要：
本文提出了OCD SLAM，一种动态立体视觉SLAM框架，它扩展了ORB-SLAM2，通过联合处理场景中的动态对象和动态特征。在动态环境中运行的常规视觉SLAM系统通常由于位姿估计和建图中使用的静态世界假设而在移动物体存在时失败。为了解决这一困境，我们引入了一种新颖的几何方法，基于视差与新提出的称为“交叉视差”的概念之间的差异，利用时间不一致性和立体不一致性来识别动态特征点。作为这种特征级运动分析的补充，OCD SLAM集成了3D目标检测模块（SMOKE）和基于卡尔曼滤波的目标跟踪，以执行对象级运动分类，从而实现对静态和动态场景元素的鲁棒分离，以实现精确的位姿估计。所提出的方法已在KITTI Odometry和KITTI Raw数据集的各种序列上进行了评估。结果表明，与ORB-SLAM2和几种最先进的动态SLAM方法相比，OCD SLAM在轨迹精度上取得了显著提升。消融研究进一步证明了交叉视差模块在KITTI Raw数据集上的有效性，并表明该方法能够检测到单独使用3D目标检测方案遗漏的动态特征。
英文摘要：
This paper presents OCD SLAM, a dynamic stereo visual SLAM framework that extends ORB-SLAM2 by jointly addressing dynamic objects and dynamic features in the scene. Usual visual SLAM systems operating in dynamic environments often fail in the presence of moving objects, due to the static-world assumption used in pose estimation and mapping. To address this predicament, we introduce a novel geometric approach based on the discrepancy between disparity and a newly proposed notion called ``cross disparity'', which exploits both temporal and stereo inconsistency to identify dynamic feature points. Complementary to this feature-level motion analysis, OCD SLAM integrates a 3D object detection module (SMOKE) with Kalman filter-based object tracking to perform object-level motion classification, enabling robust separation of static and dynamic scene elements for accurate pose estimation. The proposed approach has been evaluated on various sequences from the KITTI Odometry and KITTI Raw datasets. Results demonstrate that OCD SLAM achieves significant improvement in trajectory accuracy compared to ORB-SLAM2 and several state-of-the-art dynamic SLAM methods. Ablation studies further demonstrate the effectiveness of the cross disparity module in the KITTI Raw dataset and show that this method is able to detect dynamic features that are missed by the 3D object detection scheme alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
