Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
This work addresses the problem of efficient object representation for monocular semantic SLAM in indoor environments, but it is incremental as it builds on existing pose-graph formulations.
The paper tackles the lack of efficient object-level representation in monocular semantic SLAM by proposing a structural points representation with inverse depth parametrization for landmarks, focusing on indoor planar rectangular objects like windows and doors, and experiments in simulation show good performance in object geometry reconstruction.
Efficient object level representation for monocular semantic simultaneous localization and mapping (SLAM) still lacks a widely accepted solution. In this paper, we propose the use of an efficient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation. In particular, an inverse depth parametrization is proposed for the landmark nodes in the pose-graph to store object position, orientation and size/scale. The proposed formulation is general and it can be applied to different geometries; in this paper we focus on indoor environments where human-made artifacts commonly share a planar rectangular shape, e.g., windows, doors, cabinets, etc. The approach can be easily extended to urban scenarios where similar shapes exists as well. Experiments in simulation show good performance, particularly in object geometry reconstruction.