An Object SLAM Framework for Association, Mapping, and High-Level Tasks

TL;DR

This paper introduces a comprehensive object SLAM framework that enhances data association, object modeling, and semantic mapping, enabling autonomous robot perception and high-level task execution in complex environments.

Contribution

It proposes novel ensemble data association, outlier-robust object modeling, and an object-driven exploration strategy, advancing object SLAM capabilities for robotic applications.

Findings

Effective object association in complex conditions.

Robust object modeling with outlier resistance.

Improved autonomous mapping and scene understanding.

Abstract

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.