A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments.

A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments.
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动态环境中折反射全景相机的语义 SLAM 系统

DOI:
10.3390/s21175889
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发表时间:
2021-09-01
期刊:
Sensors (Basel, Switzerland)
影响因子:
--
通讯作者:
Lv Y
Lv Y
中科院分区:
其他
文献类型:
--
作者:
Zhang Y;Xu X;Zhang N;Lv Y

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传统的视觉SLAM系统在动态环境下工作时,会受到动态物体的干扰,性能较差。为了克服动态物体的干扰,提出了一种动态环境下折反射全景相机语义SLAM系统。使用实时实例分割网络来检测全景图像中的潜在运动目标。为了找到真实的动态目标,根据球体的极线约束对潜在的运动目标进行了验证。然后,在提取特征点时,对全景图像中的动态对象进行掩蔽。该算法只使用静态特征点来估计全景摄像机的姿态,从而提高了姿态估计的精度。为了验证我们的系统的性能,我们在公共数据集上进行了实验。实验表明,在高度动态的环境下,我们的系统的准确率明显优于传统算法。通过计算绝对弹道误差的均方根误差,我们发现我们的系统比传统的SLAM系统的性能提高了96.3%。我们的折反射全景相机语义SLAM系统在复杂的动态环境中具有更高的准确率和鲁棒性。
When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic cameras in dynamic environments. A real-time instance segmentation network is used to detect potential moving targets in the panoramic image. In order to find the real dynamic targets, potential moving targets are verified according to the sphere’s epipolar constraints. Then, when extracting feature points, the dynamic objects in the panoramic image are masked. Only static feature points are used to estimate the pose of the panoramic camera, so as to improve the accuracy of pose estimation. In order to verify the performance of our system, experiments were conducted on public data sets. The experiments showed that in a highly dynamic environment, the accuracy of our system is significantly better than traditional algorithms. By calculating the RMSE of the absolute trajectory error, we found that our system performed up to 96.3% better than traditional SLAM. Our catadioptric panoramic camera semantic SLAM system has higher accuracy and robustness in complex dynamic environments.
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