立体声和激光雷达松散合SLAM受约束的地面检测
Tian Sun1, Lei Cheng2, Ting Zhang3
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究介绍了一种结合立体视觉和LiDAR的新型混合系统,用于改进机器人技术中的3D绘图和本地化. 该方法提高了轨迹的准确性,并创建了详细的3D点云地图.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 地理空间绘制地图
背景情况:
- 准确的3D绘图对于机器人导航和避开障碍物至关重要.
- 现有的基于投影和基于voxel的3D点云处理方法具有局限性.
- 单传感器系统经常在强大的本地化和映射方面扎.
研究的目的:
- 提出使用立体视觉和LiDAR的混合本地化和绘图方法.
- 通过融合来自两个传感器的地面信息来开发姿势优化模型.
- 为了提高机器人应用的3D绘图的准确性和稳定性.
主要方法:
- 利用立体视觉提取地面特征和LiDAR张量投票数据.
- 合并视觉和LiDAR数据,以确定姿势优化的共平面性约束.
- 采用基于图形的优化和局部窗口优化来优化姿势.
主要成果:
- 与ORB-SLAM3,F-LOAM,LOAM和LEGO-LOAM相比,在轨道准确性和稳定性方面取得了显著的改进.
- 成功生成了清晰,密集的3D点云地图.
- 使用KITTI数据集验证的性能.
结论:
- 拟议的混合立体视觉和LiDAR方法为3D绘图和定位提供了卓越的性能.
- 这种方法克服了传统单传感器系统的局限性.
- 能够创建对高级机器人任务至关重要的高清3D地图.
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