Real-time onboard 6DoF localization of an indoor MAV in degraded visual environments using a RGB-D camera

Real-time and reliable localization is a prerequisite for autonomously performing high-level tasks with micro aerial vehicles(MAVs). Nowadays, most existing methods use vision system for 6DoF pose estimation, which can not work in degraded visual environments. This paper presents an onboard 6DoF pose estimation method for an indoor MAV in challenging GPS-denied degraded visual environments by using a RGB-D camera. In our system, depth images are mainly used for odometry estimation and localization. First, a fast and robust relative pose estimation (6DoF Odometry) method is proposed, which uses the range rate constraint equation and photometric error metric to get the frame-to-frame transform. Then, an absolute pose estimation (6DoF Localization) method is proposed to locate the MAV in a given 3D global map by using a particle filter. The whole localization system can run in real-time on an embedded computer with low CPU usage. We demonstrate the effectiveness of our system in extensive real environments on a customized MAV platform. The experimental results show that our localization system can robustly and accurately locate the robot in various practical challenging environments.

[1]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[2]  Daniel Maier,et al.  Real-time navigation in 3D environments based on depth camera data , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[3]  SiegwartRoland,et al.  Comparing ICP variants on real-world data sets , 2013 .

[4]  Daniel Cremers,et al.  Robust odometry estimation for RGB-D cameras , 2013, 2013 IEEE International Conference on Robotics and Automation.

[5]  Andreas Birk,et al.  Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping , 2010, IEEE Transactions on Robotics.

[6]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[7]  Zheng Fang,et al.  Experimental study of odometry estimation methods using RGB-D cameras , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Roland Siegwart,et al.  Comparing ICP variants on real-world data sets , 2013, Auton. Robots.

[9]  Albert S. Huang,et al.  Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera , 2011, ISRR.

[10]  Nicholas Roy,et al.  State estimation for aggressive flight in GPS-denied environments using onboard sensing , 2012, 2012 IEEE International Conference on Robotics and Automation.

[11]  G. Gerhart,et al.  Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments , 2009 .

[12]  Achim J. Lilienthal,et al.  Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations , 2012, Int. J. Robotics Res..

[13]  Damir Filko,et al.  Global Localization Based on 3D Planar Surface Segments , 2013, ArXiv.

[14]  Roland Siegwart,et al.  Real-time metric state estimation for modular vision-inertial systems , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Ryo Kurazume,et al.  ND voxel localization using large-scale 3D environmental map and RGB-D camera , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[16]  Albert S. Huang,et al.  Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments , 2012, Int. J. Robotics Res..

[17]  John J. Leonard,et al.  Efficient scene simulation for robust monte carlo localization using an RGB-D camera , 2012, 2012 IEEE International Conference on Robotics and Automation.

[18]  Roland Siegwart,et al.  Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[19]  Manuela M. Veloso,et al.  Depth camera based indoor mobile robot localization and navigation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[20]  John G. Harris,et al.  Rigid body motion from range image sequences , 1991, CVGIP Image Underst..

[21]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[22]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[23]  Graeme Jones,et al.  Accurate and Computationally-inexpensive Recovery of Ego-Motion using Optical Flow and Range Flow with Extended Temporal Support , 2013, BMVC.

[24]  Wolfram Burgard,et al.  Towards a navigation system for autonomous indoor flying , 2009, 2009 IEEE International Conference on Robotics and Automation.

[25]  Luca Iocchi,et al.  Autonomous Indoor Hovering with a Quadrotor , 2008 .

[26]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).