Autonomous Vision‐based Micro Air Vehicle for Indoor and Outdoor Navigation

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  • Korbinian Schmid
    Perception and Cognition, Robotics and Mechatronics Center (RMC) German Aerospace Center (DLR) Oberpfaffenhofen Germany
  • Philipp Lutz
    Autonomy and Teleoperation, Robotics and Mechatronics Center (RMC) German Aerospace Center (DLR) Oberpfaffenhofen Germany
  • Teodor Tomić
    Analysis and Control of Advanced Robotic Systems, Robotics and Mechatronics Center (RMC) German Aerospace Center (DLR) Oberpfaffenhofen Germany
  • Elmar Mair
    Perception and Cognition, Robotics and Mechatronics Center (RMC) German Aerospace Center (DLR) Oberpfaffenhofen Germany
  • Heiko Hirschmüller
    Perception and Cognition, Robotics and Mechatronics Center (RMC) German Aerospace Center (DLR) Oberpfaffenhofen Germany

Description

<jats:p>Micro air vehicles have become very popular in recent years. Autonomous navigation of such systems plays an important role in many industrial applications as well as in search‐and‐rescue scenarios. We present a quadrotor that performs autonomous navigation in complex indoor and outdoor environments. An operator selects target positions in the onboard map and the system autonomously plans an obstacle‐free path and flies to these locations. An onboard stereo camera and inertial measurement unit are the only sensors. The system is independent of external navigation aids such as GPS. No assumptions are made about the structure of the unknown environment. All navigation tasks are implemented onboard the system. A wireless connection is only used for sending images and a three‐dimensional (3D) map to the operator and to receive target locations. We discuss the hardware and software setup of the system in detail. Highlights of the implementation are the field‐programmable‐gate‐array‐based dense stereo matching of 0.5 Mpixel images at a rate of 14.6 Hz using semiglobal matching, locally drift‐free visual odometry with key frames, and sensor data fusion with compensation of measurement delays of 220 ms. We show the robustness of the approach in simulations and experiments with ground truth. We present the results of a complex, autonomous indoor/outdoor flight and the exploration of a coal mine with obstacle avoidance and 3D mapping.</jats:p>

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