Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments (1703.07334v1)

Abstract: Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.

• The paper introduces a novel real-time SLAM system that integrates monocular plane measurements for improved mapping in sparse texture settings.
• It combines plane measurements with point-based (LSD SLAM) techniques, achieving a 6.2 cm pixel depth error and 0.67% state estimation error on the TUM dataset.
• The approach advances semantic integration in SLAM, paving the way for robust dense 3D mapping applicable in robotics and autonomous navigation.

Overview of Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments

The paper "Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments" by Shichao Yang et al., presents a novel approach to address the limitations of existing SLAM algorithms in sparse texture environments. Standard SLAM methodologies often fail in these settings due to their reliance on dense features for reliable mapping and localization. This research introduces a semantic plane-based SLAM system that leverages monocular imagery to construct dense maps and improve pose estimations in low-texture environments.

Key Contributions

The primary innovations of the paper can be summarized in the following points:

• Monocular Plane SLAM: The authors propose a real-time SLAM system that uses monocular plane measurements, drawn from a pop-up 3D model applied to individual images, enhancing operation in sparse texture environments.
• Integration with Point-based SLAM: By concatenating plane measurements with point-based SLAM techniques (specifically LSD SLAM), the robustness of the system is significantly improved.
• Dense Semantic Mapping: The system is capable of generating dense semantic 3D models with high accuracy, as demonstrated on the public TUM dataset where competitors struggle.

Experimental Evaluation and Results

The algorithm was tested on the TUM dataset and two large corridors. On the TUM dataset, the proposed system's dense mapping yielded a pixel depth error of 6.2 cm and a state estimation error of 0.67% over a 60 m dataset with loops, outperforming existing SLAM technologies. These results underscore the efficacy of incorporating plane measurements for enhancing the quality of output maps and pose estimates, particularly where traditional SLAM algorithms cease to function effectively.

Implications and Future Developments

The paper illustrates the potential for integrating semantic understanding into the SLAM framework, specifically in environments where navigational scenes are devoid of significant texture features. By successfully creating dense semantic maps and improving pose estimation under these conditions, this approach can significantly impact autonomous navigation systems, particularly in robotics, where real-time environment understanding is crucial.

Looking ahead, there are a few speculated advancements:

• Hybrid Landmark Integration: The paper suggests future exploration into a unified SLAM framework that could seamlessly incorporate point, edge, and plane landmarks.
• Robotic Application: Testing and validation of the proposed SLAM system in robotic platforms to evaluate practical utility and integration with additional sensors such as IMU for improved scale accuracy.

Conclusion

The research introduces an innovative monocular SLAM framework that has the potential to advance SLAM methodologies for environments typically challenging for point-based approaches. By leveraging semantic understanding to enhance mapping quality and state estimation accuracy, this paper paves the way for more robust SLAM systems suitable for diverse operational scenarios.