In the proposed study, we describe an approach to improving the computational efficiency and robustness of visual SLAM algorithms on mobile robots with multiple cameras and limited computational power by implementing an intermediate layer between the cameras and the SLAM pipeline. In this layer, the images are classified using a ResNet18-based neural network regarding their applicability to the robot localization. The network is trained on a six-camera dataset collected in the campus of the Skolkovo Institute of Science and Technology (Skoltech). For training, we use the images and ORB features that were successfully matched with subsequent frame of the same camera ("good" keypoints or features). The results have shown that the network is able to accurately determine the optimal images for ORB-SLAM2, and implementing the proposed approach in the SLAM pipeline can help significantly increase the number of images the SLAM algorithm can localize on, and improve the overall robustness of visual SLAM. The experiments on operation time state that the proposed approach is at least 6 times faster compared to using ORB extractor and feature matcher when operated on CPU, and more than 30 times faster when run on GPU. The network evaluation has shown at least 90% accuracy in recognizing images with a big number of "good" ORB keypoints. The use of the proposed approach allowed to maintain a high number of features throughout the dataset by robustly switching from cameras with feature-poor streams.
翻译:在拟议的研究中,我们描述了一种提高多相机和有限计算力的移动机器人上视觉SLAM算法计算效率和稳健性的方法,方法是在照相机和SLAM管道之间安装中间层,在这个层中,图像使用ResNet18的神经网络进行分类,说明其对机器人本地化的适用性;在Skolkovo科学和技术研究所(Skolkovo科学和技术研究所)校园内收集的六摄像数据集方面对网络进行培训;在培训中,我们使用图像和ORB功能,这些图像和功能与随后同一相机框架(“良好”关键点或特征)成功匹配。结果显示,网络能够准确确定ORB-SLAM2的最佳图像,在SLAM2管道中实施拟议的方法,可以帮助大幅度增加SLAM算法能够本地化的图像数量,提高视觉SLAMM的全局性。在运行时间实验中,与ORB提取器提取器和功能匹配器的随后框架(“良好”键盘运行至少30倍)。