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Multi-granularity environment perception based on octree occupancy grid
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DOI：10.1007/s11042-020-09302-w.png)
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With the development of RGB-D cameras, dense point cloud model gains great attention for its information richness and obstacle avoidance features. It can be overlapped to occupancy grid for path planning and navigation applications. But there are redundant information since point cloud models tend to perceive every details in the environment and the computation complexity of traversal increases significantly with scene expansion. A possible solution is the combination of measurements of different granularities from various sensors to construct the environment models in uniform representation. Based on octree occupancy grid and our previous work, we propose a multi-granularity environment perception algorithm, which uniformly represents environment models from various sensors. A probabilistic octree representation is constructed to uniformly express the point cloud models. This representation uniformly fuses the sparse, semi-dense and dense models dynamically through an incremental algorithm along with the camera trajectory. Multiple resolutions of the same model can be obtained at any time by limiting the depth of a query. Experiments demonstrate the effectiveness of our method in minimizing trajectory error on several public available benchmarks and reducing the space complexity of environment models.
Keyword:
SLAM
Probablistic octree
Occupancy grid
Multi-granularity
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