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DA-IRRK:在视觉SLAM中进行后端优化的数据适应性代重量化强大的基于内核的方法.

Zhimin Hu1, Lan Cheng1, Jiangxia Wei1

  • 1Electrical and Power Engineering, Yingxi Campus, Taiyuan University of Technology, No. 79 Yingze West Street, Wanbailin District, Taiyuan 030024, China.

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概括

视觉同步定位和映射 (VSLAM) 的后端优化通过新的数据适应方法得到了改进. 这种方法通过更好地处理VSLAM系统中的非高斯式错误来提高轨迹的准确性.

关键词:
后端优化后端优化数据适应性的数据.循序渐进地重量化强大的核心.中位数绝对偏差的中位数视觉上的SLAM是什么意思

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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
  • 同时定位和绘制 (SLAM)

背景情况:

  • 后端优化对于最小化视觉同步定位和映射 (VSLAM) 的累积错误至关重要.
  • 当前的VSLAM框架通常采用基于内核函数的优化,假设高斯分布式再投影错误.
  • 这种假设可以导致当错误偏离高斯分布时,稳定性和准确性降低.

研究的目的:

  • 为VSLAM开发一种新的后端优化方法,解决固定的内核参数的局限性.
  • 在处理非高斯再投影错误时,提高VSLAM系统的稳定性和准确性.
  • 在VSLAM中引入数据适应方法,以进行强大的内核优化.

主要方法:

  • 提出了一种数据适应的代重量化强大内核 (DA-IRRK) 方法.
  • 集成的中位数绝对偏差 (MAD) 与代重量化策略进行适应性参数调整.
  • 在DA-IRRK框架内利用Huber内核函数进行后端优化.

主要成果:

  • 在EuRoC和KITTI数据集中的大多数序列中,DA-IRRK在轨迹准确度方面取得了显著的改进.
  • 拟议的方法在不同的VSLAM框架中显示了适应性.
  • 统计分析证实了DA-IRRK在处理非高斯噪声方面的优越能力,与现有的强大方法相比.

结论:

  • DA-IRRK方法为VSLAM后端优化提供了更强大,更准确的解决方案.
  • 根据数据特征进行稳定性参数的自适应调整是处理非高斯误差的关键.
  • 这种方法在现实场景中提高了VSLAM性能,其中错误分布可能不是严格的高斯式.