Superquadric Object Representation for Optimization-based Semantic SLAM

TL;DR

This paper presents a novel pipeline that uses semantic mask measurements from camera images to fit superquadric parameters for objects, enhancing semantic SLAM by combining machine learning and optimization techniques.

Contribution

It introduces a new method to represent semantic objects as superquadrics in optimization-based SLAM using multi-view mask data and a multi-stage fitting process.

Findings

Successfully retrieves superquadric parameters from multi-view mask data in simulations.

Evaluates different initialization strategies and cost functions for superquadric fitting.

Demonstrates potential for improved semantic SLAM with object representations.

Abstract

Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes. However, how semantic objects should be represented for an efficient inclusion in optimization-based SLAM frameworks is still an open question. Superquadrics(SQs) are an efficient and compact object representation, able to represent most common object types to a high degree, and typically retrieved from 3D point-cloud data. However, accurate 3D point-cloud data might not be available in all applications. Recent advancements in machine learning enabled robust object recognition and semantic mask measurements from camera images under many different appearance conditions. We propose a pipeline to leverage such semantic mask measurements to fit SQ parameters to multi-view camera observations using a multi-stage initialization and optimization procedure. We demonstrate the system's ability to retrieve randomly generated SQ parameters from multi-view mask observations in preliminary simulation experiments and evaluate different initialization stages and cost functions.