Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
By Xuesong Shi, Dongjiang Li, Pengpeng Zhao, Qinbin Tian, Yuxin Tian, Qiwei Long, Chunhao Zhu, Jingwei Song, Fei Qiao, Le Song and 8 more. 2019
193 citations recorded by the source corpus.
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 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 lifelong-robotic-vision.github.io/dataset/scene.
Subfields: Aerospace Engineering, Control and Systems Engineering, Mechanical Engineering
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Citations in the corpus
- Continuous Coordination As a Realistic Scenario for Lifelong Learning (2021, neutral): “There have been efforts to address this by proposing more challenging benchmarks like CORe50 (Lomonaco & Mal-toni, 2017), CRIB (Stojanov et al., 2019), OpenLoris (Shi et al., 2020), Stream51 (Roady et al., 2020), and IIRC (Ab-delsalam et al., 2020).”
- Challenges in Task Incremental Learning for Assistive Robotics (2020, methodology): “The details of our data collection, annotation, and experimental design were described in [13], [14].”