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RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion

2023-03-23 14:49:38
Ran Long, Christian Rauch, Vladimir Ivan, Tin Lun Lam, Sethu Vijayakumar


This work presents a novel RGB-D-inertial dynamic SLAM method that can enable accurate localisation when the majority of the camera view is occluded by multiple dynamic objects over a long period of time. Most dynamic SLAM approaches either remove dynamic objects as outliers when they account for a minor proportion of the visual input, or detect dynamic objects using semantic segmentation before camera tracking. Therefore, dynamic objects that cause large occlusions are difficult to detect without prior information. The remaining visual information from the static background is also not enough to support localisation when large occlusion lasts for a long period. To overcome these problems, our framework presents a robust visual-inertial bundle adjustment that simultaneously tracks camera, estimates cluster-wise dense segmentation of dynamic objects and maintains a static sparse map by combining dense and sparse features. The experiment results demonstrate that our method achieves promising localisation and object segmentation performance compared to other state-of-the-art methods in the scenario of long-term large occlusion.

Abstract (translated)

这项工作提出了一种 novel RGB-D-inertial 动态 SLAM 方法,能够在长时间内多个动态物体遮挡大部分摄像头视图的情况下实现准确的定位。大多数动态 SLAM 方法要么在动态物体占据视觉输入的较小比例时将其视为异常值并删除,要么在跟踪摄像头之前使用语义分割方法检测动态物体。因此,在没有先前信息的情况下难以检测造成大规模遮挡的动态物体。在长时间大规模遮挡的情况下,剩余的静态背景视觉信息不足以支持定位。因此,我们框架提出了一种稳健的视觉-inertial Bundle 调整方法,可以同时跟踪摄像头并估计动态物体的密集群组分割,并通过结合密集和稀疏特征维持静态稀疏地图。实验结果显示,与我们在其他长期大规模遮挡场景中使用的先进方法相比,我们的方法实现了 promising Localization 和物体分割性能。



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