VoxelPlane-Reloc: an indoor scene voxel plane relocalization algorithm

Abstract

Abstract We present VoxelPlane-Reloc, a novel indoor plane relocalization system based on voxel point clouds, designed for use with depth cameras. First, we propose an adaptive weighted plane extraction model that allows for dynamic adjustment of the correlation between points and plane accuracy. Second, we construct a plane merging model based on voxel growth, which employs a voxel neighborhood growth strategy to handle unmerged planes and allows for the merging of under-growing planes. Third, we present an incremental approach for plane input and propose a strategy for triplet selection and evaluation based on the structural constraints of the planes. This system relies solely on point clouds for relocalization and does not depend on other information, such as RGB data. We extensively evaluate the system on four datasets, and the experimental results demonstrate that the system can accurately and quickly perform relocalization with an average precision of $$99.37\%$$ 99.37 % . The time for relocalization is improved by $$92.43\%$$ 92.43 % compared to previous plane relocalization systems, and it exhibits strong robustness to indoor plane structures.

Keywords

• Relocalization
• Depth camera
• Adaptive weight function
• Plane merging model
• Structural plane constraint