DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments (1809.08379v2)

Abstract: 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 the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAM

An Analysis of DS-SLAM: A Semantic Visual SLAM for Dynamic Environments

The paper introduces a novel SLAM system, named DS-SLAM, which integrates semantic segmentation with traditional visual SLAM techniques to enhance performance in dynamic environments. This approach aims to address the deficiencies observed in existing SLAM algorithms, particularly their fragility in the presence of moving objects and their reliance on purely geometric information without semantic distinction.

System Architecture and Methodology

DS-SLAM operates using five parallel threads: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. This architecture allows for the processing of dynamic data in real-time, enhancing the robustness of pose estimation despite the presence of moving objects. The inclusion of a semantic segmentation network, working in tandem with an optical flow method, facilitates the identification and filtering of dynamic objects. The semantic octo-tree map created offers high-level task enablement by refining the SLAM's spatial understanding.

Performance Evaluation

The paper details extensive experimentation using both the TUM RGB-D dataset and real-world environments. Performance metrics such as Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) highlight DS-SLAM's superiority over ORB-SLAM2, particularly in high-dynamic sequences. Improvements are quantified as reaching up to 97% in terms of trajectory accuracy. The results validate DS-SLAM as a state-of-the-art solution under dynamic conditions, also demonstrated to work effectively on a TurtleBot2 platform with a Kinect V2 sensor.

Theoretical and Practical Implications

The implications of this research are significant for both theoretical exploration and practical deployment. The integration of semantic information into SLAM systems represents a shift towards more intelligent perception frameworks capable of discerning and reacting to environmental complexities. Practically, the system's proficiency in navigating dynamic environments could enhance a range of applications, from autonomous driving to robotic assistance in dynamic indoor spaces.

Future Developments

The work suggests avenues for future research, such as extending the range of recognizable objects within semantic segmentation networks to broaden the applicability of DS-SLAM. Additionally, optimizing the moving consistency check and enhancing real-time capabilities remain focal points for subsequent developments. The utilization of a dense semantic octo-tree map stands as a potential vector for enriching mobile robot tasks, including navigation and interaction in complex environments.

Conclusion

DS-SLAM represents a significant stride in overcoming the traditional limitations of visual SLAM systems, through its innovative use of semantic mapping to enhance robustness and accuracy in dynamic scenes. Its success in both controlled and real-world experiments underscores the system's potential as a robust navigational tool in scenarios featuring frequent environmental changes. Continued refinements could further cement its role in advancing autonomous robotic capabilities.