Marker-based Visual SLAM leveraging Hierarchical Representations (2303.01155v2)

Abstract: Fiducial markers can encode rich information about the environment and can aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detections in low-feature environments and/or for incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera along with fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset collected with a robot demonstrate that the proposed approach outperforms a traditional marker-based VSLAM baseline in terms of accuracy, given the addition of new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one reconstructed using a LiDAR SLAM approach.

• The paper presents a novel marker-based VSLAM approach that integrates hierarchical semantic representations to improve camera pose estimation and mapping accuracy.
• It leverages fiducial markers to encode semantic entities and implements novel geometric constraints, resulting in more detailed and robust indoor maps.
• Empirical tests using a monocular camera on a legged robot demonstrate superior performance over traditional VSLAM methods, closely rivaling LiDAR-based systems.

An Expert Overview of "Marker-based Visual SLAM leveraging Hierarchical Representations"

This paper presents a novel approach in the domain of Visual Simultaneous Localization and Mapping (VSLAM) by integrating marker-based systems with hierarchical representations of the environment. The proposed method utilizes a monocular camera in conjunction with fiducial markers to enhance the map reconstruction process by incorporating semantic information, leading to a richer understanding of the environment.

Technical Contributions

The primary contributions of this research are in developing a method that surpasses traditional marker-based VSLAM techniques in several key aspects. Notably, the approach not only facilitates improved camera pose estimation but also introduces semantic representation by detecting and incorporating entities like walls, corridors, and rooms encoded within fiducial markers. This advancement addresses the limitations of existing methods that largely focus on low-level geometric maps susceptible to errors in complex environments.

The methodology builds upon UcoSLAM by introducing semantic entities as components of the final map. This extension aids in generating more accurate and meaningful maps, especially when navigating complex indoor environments. The process involves establishing novel geometric constraints, such as marker-to-wall and wall-to-room relationships, thereby reducing localization errors and enhancing map detail.

Evaluation and Results

Empirical results are shown through extensive testing on a real-world dataset collected by a legged robot equipped with a monocular camera and fiducial markers strategically placed in an indoor environment. These evaluations demonstrate that the proposed VSLAM framework provides superior accuracy compared to a traditional marker-based VSLAM baseline. The performance is further validated by comparing the reconstructed maps to those generated using a LiDAR-based approach, reinforcing the accuracy of the method in environments that impose perceptual and structural challenges.

The approach achieves this by leveraging the unique ability of fiducial markers to encode semantic information, resulting in a hierarchical map that better approximates the environment's topology. This leads to an enhanced understanding and navigation capability, proving effective in situations where traditional VSLAM falls short—particularly in loop closure and localization precision.

Implications and Future Directions

The implications of this research are significant, paving the way for more robust and semantically aware SLAM systems suitable for a variety of applications including robotics, autonomous navigation, and augmented reality. By embedding hierarchical representations in VSLAM, the paper suggests a promising direction towards reducing dependency on high-cost hardware or complex structure-from-motion techniques, while still attaining high fidelity in map reconstruction.

Further developments could explore the deployment of invisible fiducial markers as envisaged by the researchers, expanding the integration of SLAM systems in environments where aesthetic or practical concerns necessitate unobtrusive markers. Additionally, enhancing computational efficiency to support real-time processing remains an open area for further refinement.

In conclusion, this paper contributes to the advancement of VSLAM by bridging geometric mapping with semantic understanding through fiducial markers. It offers a practical step forward in improving map quality, accuracy, and the breadth of information available to autonomous systems. As the field continues to evolve, combining visual and other sensory data with sophisticated algorithms will be key to the development of more adaptive and context-aware SLAM technologies.