Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

TL;DR

This paper introduces the OpenLORIS-Scene datasets and benchmarks to evaluate lifelong SLAM in dynamic, real-world indoor environments, addressing challenges of scene changes over time for service robots.

Contribution

The paper provides the first real-world indoor datasets and benchmarks specifically designed for lifelong SLAM, capturing scene dynamics over extended periods.

Findings

Datasets include multiple scene changes over time in real environments.

Benchmark metrics evaluate robustness and accuracy of pose estimation separately.

Open access to datasets and benchmarks for the research community.

Abstract

Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot's long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term \textit{lifelong SLAM} is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at https://lifelong-robotic-vision.github.io/dataset/scene.