Abstract
State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.
Highlights
Light weight Micro-Aerial Vehicles (MAVs) equipped with sensors can autonomously access environments that are difficult to access for ground robots
We present a state estimation system that utilizes long-range stereo odometry that can degrade to a monocular system at high altitude and integrates GPS, barometric and IMU
The IMU was recorded at 100 Hz; the barometer rate was 7 Hz; GPS was 4 Hz; and stereo was 10 Hz
Summary
Light weight Micro-Aerial Vehicles (MAVs) equipped with sensors can autonomously access environments that are difficult to access for ground robots. There are two main challenges for MAV autonomous navigation: (1) limited payload, power and onboard computing resources, so only light-weight compact sensors (like cameras) can be integrated for MAV applications; and (2) MAVs usually move with fast and aggressive six DOF (Degrees Of Freedom) motions. Their state estimation, environment perception and obstacle avoidance are more difficult than ground robots.
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