Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

TL;DR

This paper introduces an efficient object-level representation using structural points for monocular semantic SLAM, focusing on indoor environments with planar rectangular objects, and demonstrates promising simulation results in geometry reconstruction.

Contribution

It proposes a novel inverse depth parametrization for object landmarks in pose-graph based monocular SLAM, applicable to various geometries and environments.

Findings

Good performance in object geometry reconstruction in simulation

Effective representation for indoor objects like windows and doors

Potential extension to urban scenarios with similar shapes

Abstract

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.