Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
- URL: http://arxiv.org/abs/2001.01028v1
- Date: Sat, 4 Jan 2020 03:34:23 GMT
- Title: Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
- Authors: Zirui Zhao, Yijun Mao, Yan Ding, Pengju Ren, Nanning Zheng
- Abstract summary: We build a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from PSPNet-101 for large-scale environments.
We find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.
- Score: 47.96314050446863
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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