Papers › Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Zirui Zhao, Yijun Mao, Yan Ding, Pengju Ren, Nanning Zheng
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.
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