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维洛SLAM:紧密结合的双筒视觉-惯性SLAM与LiDAR相结合

Gang Peng^1,2, Yicheng Zhou^1,2, Lu Hu^1,2

¹School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

本研究介绍了一种Vision-IMU-2D Lidar Odometry (VILO) 算法,以提高同时定位和映射 (SLAM) 的准确性和稳定性. 维洛算法有效地融合了传感器数据,改善了机器人在具有挑战性的环境中的机器人定位.

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
传感器融合式传感器

背景情况:

现有的视觉惯性SLAM算法在具有稀疏特征的环境中或在恒定速度/纯旋转运动中难以获得准确性和稳定性.
低成本的传感器在具有挑战性的场景中往往会导致性能下降.

研究的目的:

开发一个紧密合的Vision-IMU-2D Lidar Odometry (VILO) 算法,以解决当前视觉惯性SLAM系统的局限性.
提高机器人姿势估计的准确性和稳定性,特别是在具有挑战性的环境中.

主要方法:

一个新的紧密结合的融合低成本的2D激光雷达观测与视觉惯性数据.
对激光雷达残留的雅科比矩阵的导出和构建视觉-IMU-2D激光雷达残留约束方程.
使用非线性解决方法来进行最佳的机器人姿势估计.

主要成果:

拟议的VILO算法在各种特殊环境中显示出可靠的姿势估计准确性和稳定性.
与现有方法相比,观察到位置误差和曲折角度误差的显著减少.
紧密合的融合方法有效地将2D激光雷达数据与视觉惯性信息集成在一起.

结论:

维洛算法为多传感器融合SLAM提供了显著的准确性和稳定性改进.
这种方法在视觉上具有挑战性或动态的场景中增强了机器人本地化能力.
2D激光雷达,IMU和视觉数据的紧密结合的融合为测距提供了一个强大的解决方案.

关键词:

在这里,我们可以看到LIDAR LIDAR LIDAR.多传感器融合融合技术构成估计估计的估计.视觉惯性系统的视觉惯性系统

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