Exploration of Indoor Barrier-Free Plane Intelligent Lofting System Combining BIM and Multi-Sensors

Lofting is an essential part of construction projects and the high quality of lofting is the basis of efficient construction. However, the most common method of lofting currently which uses the total station in a multi-person cooperative way consumes much manpower and time. With the rapid development of remote sensing and robot technology, using robots instead of manpower can effectively solve this problem, but few scholars study this. How to effectively combine remote sensing and robots with lofting is a challenging problem. In this paper, we propose an intelligent lofting system for indoor barrier-free plane environment, and design a high-flexibility, low-cost autonomous mobile robot platform based on single chip microcomputer, Micro Electro Mechanical Systems-Inertial Measurement Unit (MEMS-IMU), wheel encoder, and magnetometer. The robot also combines Building Information Modeling (BIM) laser lofting instrument and WIFI communication technology to get its own position. To ensure the accuracy of localization, the kinematics model of Mecanum wheel robot is built, and Extended Kalman Filter (EKF) is also used to fuse multi-sensor data. It can be seen from the final experimental results that this system can significantly improve lofting efficiency and reduce manpower.

[1]  Fengfeng Xi,et al.  Vision Based Navigation for Omni-directional Mobile Industrial Robot , 2017 .

[2]  Sukhan Lee,et al.  Vision-Based Kidnap Recovery with SLAM for Home Cleaning Robots , 2012, J. Intell. Robotic Syst..

[3]  Xin Li,et al.  An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment , 2019, Remote. Sens..

[4]  Sauro Longhi,et al.  An IMU/UWB/Vision-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment using 802.15.4a Wireless Sensor Network , 2012, Journal of Intelligent & Robotic Systems.

[5]  Ashok Kumar Patil,et al.  A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification , 2017, Sensors.

[6]  Sven Behnke,et al.  Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments , 2016, J. Intell. Robotic Syst..

[7]  İlker Ünal,et al.  Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications , 2020, Sensors.

[8]  Anton Gfrerrer Geometry and kinematics of the Mecanum wheel , 2008, Comput. Aided Geom. Des..

[9]  Yanbin Luo,et al.  Analysis of tunnel displacement accuracy with total station , 2016 .

[10]  Henrique Lorenzo,et al.  From BIM to Scan Planning and Optimization for Construction Control , 2019, Remote. Sens..

[11]  Chen Hui,et al.  Surveying and Plotting Method for the River Channel Section Based on the Total Station and CASS , 2012 .

[12]  João C. Ferreira,et al.  Beacons and BIM Models for Indoor Guidance and Location , 2018, Sensors.

[13]  A. Alsadoon,et al.  New Concept for Indoor Fire Fighting Robot , 2015 .

[14]  Ettore Stella,et al.  A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots , 2020, Sensors.

[15]  Matthias Bartholmai,et al.  Concept of a gas-sensitive nano aerial robot swarm for indoor air quality monitoring , 2018 .

[16]  Frederik Hegger,et al.  Johnny: An Autonomous Service Robot for Domestic Environments , 2011, Journal of Intelligent & Robotic Systems.

[17]  Changyin Sun,et al.  Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion , 2020, Sensors.

[18]  Dezhen Song,et al.  Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation , 2009, IEEE Transactions on Robotics.

[19]  Michael R. M. Jenkin,et al.  Inertial Sensors, GPS, and Odometry , 2008, Springer Handbook of Robotics.

[20]  Anjali K. M. De Silva,et al.  Autonomous rolling-stock coupler inspection using industrial robots , 2019 .

[21]  Avital Bechar,et al.  Agricultural robots for field operations: Concepts and components , 2016 .