IPAL: Infinite Planes as Lines for Consistent Mapping in Indoor Multifloor Environments.

Indoor high-precision maps are harder to obtain. Numerous researchers considered the extendibility of indoor plane features to constrain the poses and improve mapping consistency. However, the usage of parametric planar or line features requires complex modeling, and threshold-based matching methods are eager to lead to degradation. Indeed, planes can be more simply represented as line projections, with the projection direction perpendicular to the plane’s normal vector. In this paper, we proposed a novel mapping optimization framework that incorporates building outline features specifically designed for multi-story buildings. The framework consists of three main components: LiDAR Bundle Adjustment(LBA), global constraints based on building outline features, and factor graph optimization. The method extracts the ground only for horizontal correction, while the core idea is based on the assumption of vertical structure, projecting the point cloud contours of multi-story buildings to the top view perspective for independent spatial pose correlation, which is a strong constraint and very effective. Finally, constraints are constructed based on keyframes and intermediate frames are refined by a factor graph. Through extensive experiments conducted on multi-story buildings, we demonstrate that our algorithm significantly enhances mapping consistency and local accuracy compared to existing methods.