Floor plans are the basis of reasoning in and communicating about indoor environments. In this paper, we show that by modelling floor plans as sequences of line segments seen from a particular point of view, recent advances in autoregressive sequence modelling can be leveraged to model and predict floor plans. The line segments are canonicalized and translated to sequence of tokens and an attention-based neural network is used to fit a one-step distribution over next tokens. We fit the network to sequences derived from a set of large-scale floor plans, and demonstrate the capabilities of the model in four scenarios: novel floor plan generation, completion of partially observed floor plans, generation of floor plans from simulated sensor data, and finally, the applicability of a floor plan model in predicting the shortest distance with partial knowledge of the environment.
FloNa: Floor Plan Guided Embodied Visual Navigation
Beijing Institute of Technology（北京理工大学）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；Beijing Racobit Electronic Information Technology Co., Ltd.（北京睿科比特电子信息技术有限公司）
In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our …
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程研究中心）
We present a novel framework for floor plan-based, full six degree-of-freedom LiDAR localization. Our approach relies on robust ceiling and ground plane detection, which solves part of the pose and supports the segmentation of vertical structure elements such as walls and pillars. Our core contribution is a novel nearest neighbour data structure for an efficient look-up of nearest vertical structure elements from the floor plan. The registration is realized as a pair-wise regularized windowed pose graph optimization. Highly efficient, accurate and drift-free long-term localization is demonstra…
FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration
Suzhou Campus Southeast University（东南大学苏州校区）；Nanjing center for applied mathematics（南京应用数学中心）；School of Automation（自动化学院）；School of Cyber Science and Engineering（网络科学与工程学院）；Shenzhen Research Institute Southeast University（东南大学深圳研究院）；Samsung Electronics（三星电子）；Samsung Electronics(China) R&D Center（三星电子（中国）研发中心）
COMPASS: COmpact Multi-channel Prior-map And Scene Signature for Floor-Plan-Based Visual Localization
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg (UL)（自动化与机器人研究组，安全、可靠性与信任跨学科中心（SnT），卢森堡大学（UL））
FloorPlan-VLN: A New Paradigm for Floor Plan Guided Vision-Language Navigation
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences and School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院自动化研究所新型模式识别实验室，中国科学院人工智能学院）；AMap, Alibaba Group（阿里巴巴集团高德地图）；BAAI（北京人工智能研究院）；Xiaomi Robotics Lab（小米机器人实验室）；FiveAges
University of Freiburg（弗莱堡大学）；Toyota Research Institute（丰田研究院）
Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed, the majority of them relies on maps generated from data gathered with the same sensor modality used for localization. Typically, tedious labor by experts is needed to acquire this data, thus limiting the readiness of the system as well as its ease of installation for inexperienced operators. In this paper, we propose a memory and computationally efficient monocular camera-based localization system that allows a robot to estimate…
3D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology（信息与决策系统实验室（LIDS），麻省理工学院）；Department of Electrical and Computer Engineering, Oakland University（奥克兰大学电气与计算机工程系）
Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Plans
IDSIA, Universita della Svizzera italiana（IDSIA，瑞士意大利语大学）；Integrated Systems Laboratory / Center for Project-Based Learning - ETH Zürich（苏黎世联邦理工学院集成系统实验室/项目式学习中心）；Department of Electrical, Electronic and Information Engineering - University of Bologna（博洛尼亚大学电气、电子与信息工程系）
RAEM: Robust Autonomous Exploration for Multi-Floor Environments with a Quadruped Robot
AI Chip Center for Emerging Smart Systems（新兴智能系统AI芯片中心）；InnoHK Centers（创新香港中心）；HongKong University of Science and Technology（香港科技大学）；Southeast University（东南大学）；City University of Hong Kong（香港城市大学）；Huazhong University of Science and Technology（华中科技大学）；University of Hong Kong（香港大学）；Wuhan University（武汉大学）
University of Bonn（波恩大学）；University of Oxford（牛津大学）
Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To ov…
This paper proposes a 3D LiDAR SLAM algorithm named Ground-SLAM, which exploits grounds in structured multi-floor environments to compress the pose drift mainly caused by LiDAR measurement bias. Ground-SLAM is developed based on the well-known pose graph optimization framework. In the front-end, motion estimation is conducted using LiDAR Odometry (LO) with a novel sensor-centric sliding map introduced, which is maintained by filtering out expired features based on the model of error propagation. At each key-frame, the sliding map is recorded as a local map. The ground nearby is extracted and m…
ZONDA: Zero-shot Object Navigation with Dynamic Avoidance in Multi-floor Environments
Southern University of Science and Technology（南方科技大学）；Guangdong Direct Drive Technology Limited（广东直驱技术有限公司）；South China University of Technology（华南理工大学）；Great Bay University（大湾区大学）
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Department of Mechanical Engineering, University of California（加州大学机械工程系）
