An autonomous quadrotor for indoor exploration with laser scanner and depth camera

In this paper, we present a fully autonomous quadrotor for indoor exploration. The quadrotor is fully customized and capable of localization, mapping, planning and flying in unknown indoor environment with all real-time computations performed onboard. Two laser scanners are equipped to determine the 3D position of the quadrotor. The position measurements are further fused with Inertial Measurement Unit (IMU) to get a robust 6DOF state estimation. A depth camera is deployed to build 3D maps for the environment. Also, vision-based algorithms are designed to detect visual targets and perform precision landing. The whole system is verified in Singapore Amazing Flight Machine Competition (SAFMC). As Unmanned Systems Research Group from the National University of Singapore, we rank top in the fully autonomous category.

[1]  Fei Wang,et al.  A Robust Real-Time Vision System for Autonomous Cargo Transfer by an Unmanned Helicopter , 2015, IEEE Transactions on Industrial Electronics.

[2]  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..

[3]  Marc Pollefeys,et al.  Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle , 2014, J. Field Robotics.

[4]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[5]  Fei Wang,et al.  Guidance, navigation and control of an unmanned helicopter for automatic cargo transportation , 2014, Proceedings of the 33rd Chinese Control Conference.

[6]  Thierry Peynot,et al.  Reliable automatic camera-laser calibration , 2010, ICRA 2010.

[7]  Fei Wang,et al.  An efficient UAV navigation solution for confined but partially known indoor environments , 2014, 11th IEEE International Conference on Control & Automation (ICCA).

[8]  Huizhong Chen,et al.  Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions , 2011, 2011 18th IEEE International Conference on Image Processing.

[9]  Vijay Kumar,et al.  Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Charles Richter,et al.  Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments , 2015, Int. J. Robotics Res..

[11]  Marc Pollefeys,et al.  Vision-based autonomous mapping and exploration using a quadrotor MAV , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  David Nistér,et al.  Linear Time Maximally Stable Extremal Regions , 2008, ECCV.

[13]  Stefan Kohlbrecher,et al.  A flexible and scalable SLAM system with full 3D motion estimation , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[14]  Roland Siegwart,et al.  Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments , 2014, IEEE Robotics & Automation Magazine.

[15]  Teodor Tomic,et al.  Autonomous Vision‐based Micro Air Vehicle for Indoor and Outdoor Navigation , 2014, J. Field Robotics.