Xây dựng và thử nghiệm hệ thống thị giác và quán tính để hạ cánh tự động cho máy bay không người lái

State Estimation in Autonomous Unmanned Aerial Vehicle Landing Submitted to the Department of Aerospace Engineering, Faculty of Transportation Engineering in partial fulfillment of the requirements for the degree of Master’s of Science in Aerospace Engineering at Ho Chi Minh City University of Technology (HCMUT) - Vietnam National University HCMC (VNU-HCM) Hoang Dinh Thinh July 2020 i Intentionally left blank ii Acknowledgements I would like to express my deep appreciation of the valuable comments from my advisors: Dr Ngo Dinh Tri (Viettel Aerospace), Dr Le Thi Hong Hieu (HCMUT); my father and mother for their endless care and nurturing I also felt greatly indebted to my significant other, Tran Nhat Vy for providing me with a lot of support and my ophthalmologist, Huynh Vo Mai Quyen MD, whose talents, cordiality and graciousness have comforted me a lot during the difficult days of treatment I’m also grateful to my labmates: Nguyen Xuan Thanh Dat, Phan Minh Cuong, Huynh The Hai Nam, Tran Manh Hung, Huynh Tan Phuoc for their help with the black box and the experiments I would also like to express my sincere thanks to Dr Ben M Chen from Chinese University of Hong Kong, Dr Yan Wan from University of Texas - Arlington and other anonymous reviewers for providing me with various insights concerning the shortcomings of my work Finally, I would like express my appreciation for the work of many scientists, medical professionals, the police and military force for their collective attempt to contain the coronavirus outbreak My acknowledgement also goes to many scientists working on ophthalmic and neurologic conditions as their work brings hope and inspirations to many patients to keep carrying on Amidst the coronavirus outbreak, Ho Chi Minh City, July 2020 Hoang Dinh Thinh iii Intentionally left blank iv Abstract This thesis summarizes two papers of my work on the localization problem of a rotarywing type aircraft around a landing target, which involves estimation of the aircraft’s relative position with respect to the landing target by fusing data obtained from an Inertial Measurement Unit (IMU) and a visible-light RGB camera This information is crucial to development of a robust and consistently-performing autonomous landing control algorithm - a key to autonomous UAV fleet operation, as well as in search and rescue A unified approach comprising of three algorithms is presented with the first one designed to run when the landing target is visible to the camera and the other provide localization information to the controller when the landing target temporarily moves out of view In this manner, the landing process can be continuously performed, yielding consistent performance without cancellation or restart when the aircraft is perturbed and moves away from the helipad The last algorithm is proposed for calibration of the accelerometer For the first algorithm, a convolutional neural network was employed for recognition of the helipad in the image Combined with attitude data obtained from the IMU, localization data can be inferred This approach is indifferent to different helipad designs and landing target and capable of performing in real-time on a Raspberry Pi 3B computer Experiments show that while the estimation is noisy, it is unbiased and expressed decent agreement with estimation from the fiducal marker approach such as ARUCOTag in particular For the second algorithm, we developed a Linear Time Varying (LTV) Kalman Filter for localizing the aircraft based on optical flow and IMU gyroscope and accelerometer data Through transformation via virtual measurements, the LTV measurement model v allows a much more simplified analytical analysis compared to linearization of camera’s projection model in EKF approaches We provided proof for a theorem that extends the result of contraction analysis for general Hamiltonian systems to encompass secondorder dynamics, resulting in stability of the filter for assimilating acceleration, rather than velocity data directly through the process model The algorithm demonstrated excellent localization performance in the time window of 30 seconds without loop closure techniques, light complexity compared to fiducial marker approaches and show agreement with monocular ORB-SLAM2, a state-of-the-art SLAM We also developed a prototyping device and detailed the accelerometer calibration process, with the development of a novel calibration algorithm that operates with several ARUCOTags Although rigorous testing of this calibration method was not performed, the calibrated accelerometer has very little residual gravity and contributed significantly to estimation of the accurate scale in aforementioned localization algorithms Keywords: localization, SLAM, estimation theory vi Commitment I hereby commit that this is my scientific work The results are presented as-is and have never been published anywhere else, except on the papers that I authored Under no circumstance did I intentionally claim facts from other people’s work without attributing them properly in the bibliography Hoang Dinh Thinh vii CHAPTER IMPLEMENTATION AND CALIBRATION Table 5.2: Calibration result Parameters X axis misalignment Y axis misalignment Z axis misalignment Scale X Scale Y Scale Z Bias X Bias Y Bias Z Values -0.00573419838339676 -0.00186736890453765 -0.00900500887137062 1.20767818666729 1.16825035803752 1.16585007950014 -0.214331157891075 -0.116907377183635 0.332252604436723 Note that if Ψi matrices are known in advance, the optimization problem is convex, and a unique solution can be found Equation 5.3 can easily be transformed into a residual term, and least squares method are then employed In practice, we performed 50 runs, each lasted for 2-3 seconds and moved the IMU in all directions The calibration result is obtained with the Trust Region Reflective method Implementation of this calibration algorithm is included in the repository shown at the end of this chapter, in the folder IMUCALIB The calibration result is shown in Table 5.2 The αz x, αyz , αzy terms are small, close to zero which validate our assumptions of small angle approximation The comparison of gravitational acceleration before and after calibration when the IMU was left resting at different attitude is shown in Figure 5.3 5.3 Data acquisition The data acquisition module is programmed in Python 3, using picamera library for communication with the Pi camera and RTEMISLib to communicate with the IMU Since the RTEMISLib already included compensation for the gyroscope and acceleration values, the calibrator can work with these values straight out of the box Because the Pi camera uses significant buffer memory for storing the image, we encountered a significant lag when running RTEMISLib and picamera on the same thread Decision was made to separate these two modules into different processes and run on different CPU cores The images and IMU readings were saved at a frequency 73 5.4 CAMERA CALIBRATION Figure 5.3: Gravitational acceleration before and after calibration of, on average, 10 times/sec and 100 times/sec However, the sampling time of the two sensors are not constant, but vary a little bit However, resampling of data was not necessary since all the expressions above were written in the time-varying form The DAQ code can be found in the daq main.py file 5.4 Camera calibration The camera was calibrated following the tutorial [92] 5.5 Algorithm implementation The algorithms presented in Chapter 3, and were implemented in Python with numpy library However, the calibration of the IMU was done in MATLAB 74 CHAPTER IMPLEMENTATION AND CALIBRATION 5.6 Open-source release All the implementation code for this thesis can be found at the following repositories: • https://github.com/thinhhoang95/RTIMULib2 • https://github.com/thinhhoang95/helipad-yolo • https://github.com/thinhhoang95/panacea-astro • https://github.com/thinhhoang95/panacea 75 Chapter Conclusion 6.1 Conclusion In this thesis, we have presented three approaches for the problem of helipad localization in autonomous landing for two circumstances The first algorithm is to estimate the helipad’s position when the helipad is visible to the camera with deep learning and simple constraints derived from the projection of an object whose size was known in advance The second algorithm provide localization information based on filtering with a novel analytical proof on the neccessary condition for stability Finally, an accelerometer calibration algorithm was presented to help cancel out the residual acceleration due to gravity Although the UAV control problem (that is, to autonomously land the aircraft on the helipad) was not studied in this thesis, it was addressed in the work of Nam Huynh’s graduation thesis which I co-developed [76] These results differ from available approaches in SLAM and autonomous landing as they present a holistic solution to localization in both circumstances: landing target visible and landing target out of sight These work also contributes to the understanding about analytical behaviour of LTV Kalman Filter, expanding the literature of contraction analysis and paved the way for interesting engagements of drones in both economic and social missions 76 6.2 FUTURE WORK 6.2 77 Future Work As these results came off from theoretical analysis and implementations were available, the filtering and optimization code will be subjected to a lot of optimization, for example, to exploit the sparsity of the matrices and perform parallel computing to speed up computation time On the theoretical side, the behavior of the covariance matrix of the LTV Filter proposed should be studied in case of non-uniform observability condition cannot be established due to insufficient feature observation Bibliography [1] Skybrary (2019) Unmanned aerial systems (uas), [Online] Available: https: / / www skybrary aero / index php / Unmanned _ Aerial _ Systems _ (UAS ) #Definitions [2] C A 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Gaussian The Kalman Filter is often regarded as the analytical solution to the general class of Bayes filter under such assumptions, and spurred a whole class of variants that apply to different... and N (0, R) respectively Very often, the central limit theorem (CLT) serves as the ground for choice of the normal distribution for the noise terms Define the a priori state estimate at time