Memory-Efficient Boundary Map for Large-Scale Occupancy Grid Mapping
🤖 AI Summary
This work addresses the excessive memory consumption of conventional occupancy grid maps when deployed at high resolutions or large scales. To mitigate this, the authors propose a boundary-based map representation that explicitly stores only boundary voxels—such as occupied and frontier voxels—while implicitly encoding interior free and exterior unknown regions via two-dimensional closed surfaces. An efficient mechanism for occupancy state querying and updating is devised, integrated within a global–local mapping framework to enable real-time construction. Additionally, specialized data structures are introduced to enhance operational efficiency. The proposed method substantially reduces memory footprint while supporting efficient map construction, updates, and queries under real-time sensor input. The implementation has been made publicly available.
Application Category
📝 Abstract
Determining the occupancy status of locations in the environment is a fundamental task for safety-critical robotic applications. Traditional occupancy grid mapping methods subdivide the environment into a grid of voxels, each associated with one of three occupancy states: free, occupied, or unknown. These methods explicitly maintain all voxels within the mapped volume and determine the occupancy state of a location by directly querying the corresponding voxel that the location falls within. However, maintaining all grid voxels in high-resolution and large-scale scenarios requires substantial memory resources. In this paper, we introduce a novel representation that only maintains the boundary of the mapped volume. Specifically, we explicitly represent the boundary voxels, such as the occupied voxels and frontier voxels, while free and unknown voxels are automatically represented by volumes within or outside the boundary, respectively. As our representation maintains only a closed surface in two-dimensional (2D) space, instead of the entire volume in three-dimensional (3D) space, it significantly reduces memory consumption. Then, based on this 2D representation, we propose a method to determine the occupancy state of arbitrary locations in the 3D environment. We term this method as boundary map. Besides, we design a novel data structure for maintaining the boundary map, supporting efficient occupancy state queries. Theoretical analyses of the occupancy state query algorithm are also provided. Furthermore, to enable efficient construction and updates of the boundary map from the real-time sensor measurements, we propose a global-local mapping framework and corresponding update algorithms. Finally, we will make our implementation of the boundary map open-source on GitHub to benefit the community:https://github.com/hku-mars/BDM.
Problem
Research questions and friction points this paper is trying to address.
occupancy grid mapping
memory efficiency
large-scale environments
boundary representation
robotic perception
Innovation
Methods, ideas, or system contributions that make the work stand out.
boundary map
memory-efficient mapping
occupancy grid
large-scale SLAM
surface-based representation
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