SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

TL;DR

This paper introduces SQ-SLAM, a monocular semantic SLAM system that uses superquadrics for more accurate object shape representation, improving map quality and object localization in real-time.

Contribution

It proposes a novel superquadric-based object representation and a parameter estimation method, enhancing accuracy and adaptability in semantic SLAM.

Findings

Accurate object pose estimation using superquadrics

Enhanced object map accuracy compared to previous methods

Real-time performance demonstrated on public datasets

Abstract

Object SLAM uses additional semantic information to detect and map objects in the scene, in order to improve the system's perception and map representation capabilities. Quadrics and cubes are often used to represent objects, but their single shape limits the accuracy of object map and thus affects the application of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape parameters into SLAM for representing objects, and propose a separate parameter estimation method that can accurately estimate object pose and adapt to different shapes. Furthermore, we present a lightweight data association strategy for correctly associating semantic observations in multiple views with object landmarks. We implement a monocular semantic SLAM system with real-time performance and conduct comprehensive experiments on public datasets. The results show that our method is able to build accurate object map and has advantages in object representation. Code will be released upon acceptance.