Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps (1709.05533v2)

Abstract: Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies the navigation task by providing a map to the robot which is tailored for path planning use. This novel approach transforms a sparse feature-based map from a visual Simultaneous Localization And Mapping (SLAM) system into a three-dimensional topological map. This is done in two steps. First, we extract occupancy information directly from the noisy sparse point cloud. Then, we create a set of convex free-space clusters, which are the vertices of the topological map. We show that this representation improves the efficiency of global planning, and we provide a complete derivation of our algorithm. Planning experiments on real world datasets demonstrate that we achieve similar performance as RRT* with significantly lower computation times and storage requirements. Finally, we test our algorithm on a mobile robotic platform to prove its advantages.

• The paper presents Topomap, a framework that transforms sparse visual SLAM data into convex free-space clusters for improved robot navigation.
• It employs a two-step process using TSDF-based occupancy extraction and iterative cluster merging to form an efficient navigation graph.
• Experimental evaluations show that Topomap achieves near-optimal path quality with orders of magnitude reduction in computational demands.

An Overview of Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

The paper presents "Topomap," a framework designed to improve mobile robot navigation within large-scale, semi-structured environments. This system addresses various challenges associated with traditional navigation approaches, such as high computational demands and limited understanding of traversable spaces, by providing an innovative approach to generating topological maps from visual Simultaneous Localization and Mapping (SLAM) data. The paper details the methodology and the resulting improvements in efficiency, particularly in global path planning, without relying on heavy computational resources or sensor setups.

Topomap introduces a two-step process for creating a three-dimensional topological map from a sparse, feature-based SLAM map. Initially, it extracts occupancy information from a sparse point cloud and then forms a set of convex free-space clusters, which serve as vertices in the topological map. This representation enhances global planning efficiency while requiring significantly less computation time and storage compared to methods like RRT*. The paper includes a comprehensive derivation of the underpinning algorithm and demonstrates its performance through experiments on real-world datasets, ultimately validating the framework's advantages using a mobile robotic platform.

Methodological Insights

1. Map Representation and Occupancy Extraction: The Topomap framework represents the environment's topological map by segmenting free space into convex regions. This segmentation exploits Truncated Signed Distance Fields (TSDFs), primarily derived from sparse SLAM landmarks. The method manages to create reliable maps by processing these landmarks, even in the face of noise and sparsity.
2. Cluster Formation and Merging: Clusters are grown and subsequently merged based on convexity and free-space criteria. Starting with cluster centers seeded along the trajectory, the system performs iterative voxel additions respecting compactness and convexity requirements. Merging adjacent clusters with minimal obstacle inclusion refines the map, effectively capturing navigable space even in complex environments.
3. Topological Navigation and Path Planning: The core merit of Topomap lies in its ability to facilitate efficient path planning using graph-based methodologies such as A*, which are considerably less computationally demanding than traditional methods. By representing the space as a navigation graph consisting of cluster edges and portals, the framework elegantly simplifies robot traversal within the mapped environment.

Experimental Evaluation and Performance Metrics

The Topomap framework's capabilities are rigorously assessed against both qualitative and quantitative benchmarks. Comparisons are made against traditional grid-based methodologies using high-fidelity TSDF maps. Topomap consistently exhibits marginal path length deviations while achieving orders of magnitude reductions in compute times. Additionally, tests on a Turtlebot validate its real-world applicability, demonstrating successful navigation with minimal hardware dependencies.

Practical and Theoretical Implications

Practically, Topomap positions itself as a key contributor to enhancing robot autonomy in resource-constrained environments. By sidestepping the need for costly sensors and extensive computational resources, the framework lowers the barrier to deploying autonomous systems in various consumer and industrial applications. Theoretically, the research underscores the potential of sparse feature-based mapping techniques in managing uncertainties and scaled-down computational demands, paving the way for future exploration in larger and more complex environments.

Future Directions

The authors indicate potential extensions of their research, including testing on larger environments and integration with aerial platforms. Incorporating semantic information within the maps is another promising avenue, likely to advance robotic autonomy further by enriching the contextual understanding of the navigation environment.

In conclusion, Topomap presents a notable advancement in mobile robot navigation, offering a streamlined and efficient alternative to traditional SLAM-based methods. Its ability to operate effectively under constrained conditions presents meaningful implications for the future development and deployment of autonomous systems.