DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

By Chao Yu, Zuxin Liu, Xinjun Liu, Fugui Xie, Yi Yang, Qi Wei, Fei Qiao. IEEE/RJS International Conference on Intelligent RObots and Systems, 2018

1,029 citations recorded by the source corpus.

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5eee4151201258ca75202057baf00a9774ccef38

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Citations in the corpus

Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM (2019, background): “DS-SLAM improves over ORB SLAM2 by removing features on moving objects [18].”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, neutral): “An agent with autonomous exploration and decision-making capabilities must not only consider which actions to take to explore the environment, but also determine when to stop exploring to answer questions.”
SuMa++: Efficient LiDAR-based Semantic SLAM (2019, methodology): “Motivated by the advances of deep learning and Convolutional Neural Networks (CNNs) for scene understanding, there have been many semantic SLAM techniques exploiting this information using cameras [5], [31], cameras + IMU data [4], stereo cameras [10], [15], [18], [33], [38], or RGB-D sensors [3], [”