DOT-SLAM:一种立体视觉同时定位和映射 (SLAM) 系统,具有基于图形优化的动态对象跟踪
Yuan Zhu1, Hao An1, Huaide Wang1
1School of Automotive Studies, Tongji University, Shanghai 201800, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了DOT-SLAM,这是一个视觉SLAM系统,通过跟踪动态物体来提高自动驾驶汽车定位精度. 它有效地使用静态和运动特征来精确地估计自我车辆的姿势.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 自主系统 自主系统
背景情况:
- 视觉SLAM系统通常假设静态环境,导致动态对象的精度降低.
- 动态物体,特别是车辆,在自动驾驶中显著降低了本地化性能.
研究的目的:
- 开发一个新的立体视觉SLAM系统 (DOT-SLAM),集成动态对象跟踪.
- 为了提高自我车辆定位的准确性,并在动态物体的存在下生成静态环境地图.
主要方法:
- 集成的动态对象使用图形优化进行姿势估计.
- 采用粗微深度估计方法,利用相机-道路平面几何学.
- 利用路平面和非全学约束来准确的动态对象初始化.
- 通过图形优化,共同优化自我-车辆姿势,动态物体姿势,前景/背景点和道路平面.
主要成果:
- DOT-SLAM有效地利用了动态对象和背景的特征.
- 与传统方法相比,实现了更准确的车辆轨迹估计.
- 生成了一个静态环境地图,证明了改进的本地化性能.
- 在KITTI-360数据集和现实世界的测试中验证了有效性.
结论:
- 对于自动驾驶汽车来说,DOT-SLAM在动态环境中显著提高了定位精度.
- 该系统成功地集成了动态对象信息,以实现强大的SLAM.
- 在复杂的现实场景中提供了可靠的导航解决方案.
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