HiRo-SLAM:一个高精度和强大的视觉惯性SLAM系统,具有精确的摄像头投影建模和自适应特征选择
Yujuan Deng1,2,3, Liang Tian4, Xiaohui Hou5
1School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
HiRo-SLAM是一种新的视觉惯性SLAM系统,通过整合精确的摄像机建模,自适应性特征抑制,强大的优化和点线特征融合,提高了准确性和稳定性. 这种先进的系统在具有挑战性的环境中显著减少了轨迹误差.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 同时定位和绘制 (SLAM)
背景情况:
- 传统的视觉惯性SLAM方法由于不完美的摄像机模型,不均的特征分布和异常值而存在偏差.
- 这些局限性阻碍了现实应用中的准确性和稳定性.
研究的目的:
- 引入HiRo-SLAM,这是一个统一的优化框架,用于高精度和强大的视觉惯性SLAM.
- 通过新的创新来解决现有的SLAM系统的局限性.
主要方法:
- 精确的摄像头投影建模 (PCPM) 为准确的摄像头内在和扭曲处理.
- 基于可见度金字塔的适应性非最大抑制 (P-ANMS) 对于统一的特征约束.
- 使用渐进非凸度 (GNC) 来减轻异常影响的强大优化.
- 点线特征融合前端结合点线特征以增强感知.
主要成果:
- 在EuRoC MAV,TUM-VI和OIVIO基准上,HiRo-SLAM的性能优于最先进的视觉惯性SLAM方法.
- 在EuRoC MAV数据集上实现了30.0%的绝对轨迹误差减少.
- 在受控条件下证明了毫米级准确性.
结论:
- HiRo-SLAM为视觉惯性SLAM提供了显著的准确性和稳定性的改进.
- 该系统在适度纹理和最小运动模糊的环境中表现出色.
- 在具有严重运动模糊或极端照明的高度动态场景中,性能可能会受到限制.
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