Fluorescent patterns with multiple functions enable high-security anti-counterfeiting labels. Complex material synthesis and patterning processes limit the application of multifunctional fluorescent patterns, so the technology of in situ fluorescent patterning with tunable multimodal capabilities is becoming more necessary. In this work, an in situ fluorescent patterning technology was developed using laser direct writing on solid cellulose film at ambient conditions without masks. The fluorescent intensity and surface microstructure of the patterns could be adjusted by programmable varying of the laser parameters simultaneously. During laser direct writing, carbon dots are generated in situ in a cellulose ester polymer matrix, which significantly simplifies the fluorescent patterning process and reduces the manufacturing cost. Interestingly, the tunable fluorescent intensity empowers the fabrication of visual stereoscopic fluorescent patterns with excitation dependence, further improving its anti-counterfeiting performance. The obtained fluorescent patterns still show ultrahigh optical properties after being immersed in an acid/base solution (pH 5-12) over one month. In addition, the anti-UV performance of the obtained laser-patterned film with transmittance around 90% is comparable to that of commercial UV-resistant films. This work provided an advanced and feasible approach to fabricating programmable, performance-tunable, subtle fluorescent patterns in large-scale for industrial application.

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Pose estimation provides accurate position and orientation information of the intelligent agents in real time. The accuracy of the estimation directly affects the performance of sequential tasks such as mapping, motion planning, and control. EKF (Extended Kalman Filter) is a standard theory for nonlinear pose estimation by modeling state uncertainty to Gaussian distribution. However, EKF has requirements for proper initial estimate and system noise to obtain bounded optimal estimate. Meanwhile, model nonlinearity and non-gaussian noise modeling affect the performance of EKF significantly in practical applications. In this thesis, we focus on improving the performance of nonlinear pose estimation by reinforcement learning. By formulating an EKF measurement update as a Markov Decision Process (MDP), reinforcement learning agents can be trained to learn the estimator gain through data samples and executed as the online estimator for pose estimation tasks.

Based on the above idea, we propose a novel reinforcement learning-compensated EKF estimator (RLC-EKF), where the RL agent serves as a second-time measurement update that subsides the residual error from the standard EKF estimate. The estimator is developed and testified on two specific pose estimation scenarios. Firstly, as a continuous work from the previous study, a framework for 3 DOF orientation estimation using inertial sensor and magnetometer is replicated. Then, the framework is extended by different RL algorithms training and multi-scale robustness validation. Besides, we implement the estimator on a feature-based 2D plane localization framework. The proposed framework shows the feasibility of the underlying algorithm on a localization task with a known map. As a result, the RLC-EKF estimator gives superior performance and convincible robustness compared to classical methods in severe conditions such as varying initial states, degree of noise intensities, and model covariance.