Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.

翻译：视觉里程计（VO）在自主系统中扮演关键角色，其主要挑战在于相机图像缺乏深度信息。本文提出OCC-VO这一新型框架，利用深度学习的最新进展将2D相机图像转化为3D语义占据，从而规避了传统方法中同时估计自车位姿和路标位置的难题。在该框架内，我们采用TPV-Former将环视相机图像转换为3D语义占据。针对转换带来的挑战，我们专门设计了一套位姿估计与建图算法，包含语义标签滤波器、动态物体滤波器，并最终利用体素滤波器维护一致的全局语义地图。在Occ3D-nuScenes数据集上的评估表明，与ORB-SLAM3相比，本方法在成功率上提升20.6%，轨迹精度提高29.6%，同时突出展示了构建综合地图的能力。我们的实现已开源，代码可见：https://github.com/USTCLH/OCC-VO。