推进全球姿势精细化:通过四互波来实现封闭电路的线性无参数模型
Rubens Antônio Leite Benevides1, Daniel Rodrigues Dos Santos2, Nadisson Luis Pavan3
1Polytechnic Center, Federal University of Parana, Curitiba 81530-000, PR, Brazil.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究引入了一种新的线性模型,用于基于LIDAR的同时定位和绘图 (SLAM) 系统的全球姿势改进. 它有效地使用闭路方法和球形线性插值 (SLERP) 来纠正轨迹漂移,以获得准确的3D姿势估计.
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
- 地理学就是地质学.
背景情况:
- 全球姿势的精细化对于同时定位和映射 (SLAM) 系统至关重要,特别是那些使用光探测和射程 (LIDAR) 的系统.
- 由于连续的3D点云注册造成的姿势估计的偏移,导致平台路径计算中的不准确性.
- 现有的方法经常在大型环境中与显著的姿势差异和计算效率作斗争.
研究的目的:
- 在基于LIDAR的SLAM中提出一种新的,线性和无参数的模型,用于全球姿势的精细化.
- 为了应对轨道偏移校正的挑战,并提高3D姿势估计的准确性.
- 引入一个高效的粗细对对注册技术.
主要方法:
- 一个线性,无参数的模型,利用一个封闭电路进行全球轨迹校正.
- 将旋转映射到四次元,并使用球形线性插值 (SLERP) 进行旋转过渡.
- 最小方形 (LS) 方法用于旋转闭合约束,以及用于转换调整的独特线性相.
- 一个从粗到细的双向注册,集成快速全球注册和通用ICP与多尺度采样和过.
主要成果:
- 拟议的模型证明了在不同点云数据集 (移动和地面激光扫描仪) 中对3D姿势估计的有效性.
- 显著的姿势差异得到了成功的管理,表明了强大的表现.
- 观察到有效的姿势优化,特别是在更大的轨迹电路中.
结论:
- 开发的线性模型为LIDAR-SLAM的全球姿势改进提供了强大的和高效的解决方案.
- 基于SLERP和LS的约束的集成提供了准确的轨迹校正.
- 粗细的注册方法提高了SLAM系统的整体性能和适用性.
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