Office5 Trajectory

Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.CoRL 2021论文《面向室内定位的自改进语义感知》（Self-Improving Semantic Perception for Indoor Localisation）数据集对应的摘要内容如下：本文提出一种可在部署阶段持续优化感知能力的新型机器人系统。与现有从大规模数据集中学习语义并部署固定模型的经典范式不同，我们提出一种可在机器人端持续更新语义模型以适配部署环境的框架。通过结合持续学习与自监督学习，本机器人系统可在无外部监督的情况下于部署阶段在线学习。我们在搭载3D平面图定位功能的机器人上开展了真实世界实验，实验结果表明，即便在截然不同的环境中部署，机器人的语义感知能力在部署阶段得到了优化，且这一优化可有效提升定位性能。我们进一步探究了此类持续学习场景所面临的灾难性遗忘风险，研究发现经验回放是缓解遗忘现象的有效手段，且即便在不同环境间切换时，本机器人系统仍可实现性能优化。与部署固定模型的方案相比，本系统的分割任务性能平均提升60%，定位精度平均提升10%，且在适配更多新环境的过程中可始终维持该性能增益。