IMU and Multiple RGB-D Camera Fusion for Assisting Indoor Stop-and-Go 3D Terrestrial Laser Scanning

Robotics

Jacky C.K. Chow¹  Derek D. Lichti¹  Jeroen D. Hol²  Giovanni Bellusci² 

[1] Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada; E-Mail:;Xsens Technologies B.V., Pantheon 6a, Enschede 7521 PR, The Netherlands; E-Mails:

关键词: SLAM;    Kinect;    IMU;    LiDAR;    ICP;    Kalman filter;    visual odometry;    monocular vision;    tightly-coupled;    navigation;   

DOI  :  10.3390/robotics3030247

来源: mdpi

【 摘 要 】

Autonomous Simultaneous Localization and Mapping (SLAM) is an important topic in many engineering fields. Since stop-and-go systems are typically slow and full-kinematic systems may lack accuracy and integrity, this paper presents a novel hybrid “continuous stop-and-go” mobile mapping system called Scannect. A 3D terrestrial LiDAR system is integrated with a MEMS IMU and two Microsoft Kinect sensors to map indoor urban environments. The Kinects’ depth maps were processed using a new point-to-plane ICP that minimizes the reprojection error of the infrared camera and projector pair in an implicit iterative extended Kalman filter (IEKF). A new formulation of the 5-point visual odometry method is tightly coupled in the implicit IEKF without increasing the dimensions of the state space. The Scannect can map and navigate in areas with textureless walls and provides an effective means for mapping large areas with lots of occlusions. Mapping long corridors (total travel distance of 120 m) took approximately 30 minutes and achieved a Mean Radial Spherical Error of 17 cm before smoothing or global optimization.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.