Vision Based Pose Estimation And Navigation For A Quadrotor

Nowadays, the research about quadrotor has got much attention in the UAV(Unmaned Aerial Vehicle) field. It has great significance and wide prospect of applications. Vision based motion and pose estimation, localization and navigation for a quadrotor are the fundamental and key problems in research. Up to now, there have no satisfied monocular visual localization and navigation systems for quadrotors in the world. The main work in this paper is to have a research on the problems, which are vision based multi-sensor fusion motion and pose estimation, localization and navigation problems for a quadrotor with limited payload, power and computational resource.The motion estimation and pose estimation of the quadrotor are the foundation and key for various missions. The vision based motion estimation and pose estimation approaches are mainly external vision based algorithms and onboard vision based algorithms. The external vision based algorithms are more reliable and robust, but have limited region. They could be used for the autonomous take-off and landing of a quadrotor, or the precision control. Onboard vision based approaches for the quadrotor are paid much attention to because of the flexibility and getting rid of limited region. They could be used for various and more complex missions.In real applications, the quadrotor might work in a large-scale region. Based on our former algorithms, we extend the existing approaches in limited region to a larger region. We have some research on the vision base localization and navigation for the quadrotor. The main work and contribution of this paper are as follows:(1) We give the external vision based robust and accurate pose estimation system for the quadrotor, and perform in real experiments. Our system could work well in outdoor environments, while most existing systems could only perform in indoor environments. One key characteristic of our system is that we only use the quadrotor’s own four rotors as the visual features, which is more reliable and robust than colored blobs and LEDs in outdoor environments. When all the four rotors of the quadrotor are observed rightly, we present the fast and accurate pose estimation algorithm EMRPP for the coplanar point problem. It gets the initial pose guess by non-iterative EPnP algorithm. By using the preliminary position result calculated by former vision step, we have modified the RPP algorithm and got the fast and accurate results of pose estimation. When the four rotors are observed partly, most of the existing approaches don’t mention this case and could not get right results. By using the vision data and the onboard IMU, we propose the IMU+3P and IMU+2P algorithms which could resolve this case and get fast and right pose estimation results. Taking full advantage of our former proposed algorithms, the pose estimation system could be used for the autonomous take-off and landing of a quadrotor or the precision control.(2) Considering the pinpoint landing of the quadrotor, we present the landmark based fast and accurate pose estimation algorithm EIRPP. It makes use of onboard vision and IMU data, utilizing these data in EPnP to get initial guess and improve the RPP algorithms. We get fast and accurate pose estimation results in the end.(3) Making the best of the omni-directional flight characteristic of the quadrotor, we propose the BRISK based fast motion estimation algorithm which uses the natural features and the onboard IMU. Considering the hovering flight of the quadrotor, most approaches utilize the optical flow algorithms. The optical flow algorithms could only obtain the velocity information and the hovering point might drift along the time. Using the BRISK based fast motion estimation algorithm, we realize the fast spot hovering of the quadrotor. For the general conditions, we present the natural features based fast and accurate pose estimation algorithm for the quadrotor which has limited pay load and computational resource. This algorithm makes use of the onboard camera, IMU and sonar. It could work for both non-planar and planar scenes and solves the initialization problem and the metric scale estimation problem of the monocular system effectively. By using the data from the IMU, the pose estimation problem is simplified and obtains more fast and accurate results of pose estimation.(4) Considering the limited payload, power and computational resources, we discuss the hardware platform construction for the quadrotor. We have presented a multi-sensor fusion based monocular visual localization and navigation system for the quadrotor. This system with an IMU, a sonar and a monocular down-looking camera as its main sensor is able to work well in GPS-denied and markerless environments. Different from common keyframe-based system, our visual localization and navigation system is based on both keyframes and keypoints. Considering the accuracy and computational time, GPU-based SURF is performed for feature detection and feature matching. The fast motion estimation algorithm and the multilevel motion judgment rule are presented for updating the keyframes and keypoints. This is beneficial to hovering or near-hovering conditions and could reduce the error accumulation effectively. The general monocular visual systems usually lack the metric scale. By using sonar data, we solve the metric scale estimation problem and get good initialization of the navigation system. The good features selected, RANSAC, Local bundle adjustment and some other measures are performed to reduce the error accumulation and optimize the results.In the end, we have realized the monocular localization and navigation system for the quadrotor.