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首页> 外文会议>International conference on graphic and image processing >Loose fusion based on SLAM and IMU for indoor environment
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Loose fusion based on SLAM and IMU for indoor environment

机译：基于SLAM和IMU的室内环境松散融合

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摘要

The simultaneous localization and mapping (SLAM) method based on the RGB-D sensor is widely researched in recent years. However, the accuracy of the RGB-D SLAM relies heavily on correspondence feature points, and the position would be lost in case of scenes with sparse textures. Therefore, plenty of fusion methods using the RGB-D information and inertial measurement unit (IMU) data have investigated to improve the accuracy of SLAM system. However, these fusion methods usually do not take into account the size of matched feature points. The pose estimation calculated by RGB-D information may not be accurate while the number of correct matches is too few. Thus, considering the impact of matches in SLAM system and the problem of missing position in scenes with few textures, a loose fusion method combining RGB-D with IMU is proposed in this paper. In the proposed method, we design a loose fusion strategy based on the RGB-D camera information and IMU data, which is to utilize the IMU data for position estimation when the corresponding point matches are quite few. While there are a lot of matches, the RGB-D information is still used to estimate position. The final pose would be optimized by General Graph Optimization (g2o) framework to reduce error. The experimental results show that the proposed method is better than the RGB-D camera's method. And this method can continue working stably for indoor environment with sparse textures in the SLAM system.
机译：近年来，基于RGB-D传感器的同时定位与映射（SLAM）方法得到了广泛的研究。但是，RGB-D SLAM的精度在很大程度上取决于对应的特征点，并且在具有稀疏纹理的场景中会丢失位置。因此，已经研究了许多使用RGB-D信息和惯性测量单位（IMU）数据的融合方法，以提高SLAM系统的精度。但是，这些融合方法通常不考虑匹配特征点的大小。当正确匹配的数量太少时，由RGB-D信息计算出的姿势估计可能不准确。因此，考虑到SLAM系统中匹配的影响以及纹理少的场景中位置丢失的问题，提出了一种结合RGB-D和IMU的松散融合方法。在提出的方法中，我们设计了一种基于RGB-D摄像机信息和IMU数据的松散融合策略，即当对应点匹配很少时，利用IMU数据进行位置估计。尽管有很多匹配项，但RGB-D信息仍用于估计位置。最终姿态将通过通用图优化（g2o）框架进行优化，以减少错误。实验结果表明，该方法优于RGB-D相机的方法。并且该方法可以继续在SLAM系统中具有稀疏纹理的室内环境中稳定工作。

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