This paper presents a fast and robust plane feature extraction and matching technique for RGB-D type sensors. We propose three algorithm components required to utilize the plane features in an online Simultaneous Localization and Mapping (SLAM) problem: fast plane extraction, frame-to-frame constraint estimation, and plane merging. For the fast plane extraction, we estimate local surface normals and curvatures by a simple spherical model and then segment points using a modified flood fill algorithm. In plane parameter estimation, we suggest a new uncertainty estimation method which is robust against the measurement bias, and also introduce a fast boundary modeling method. We associate the plane features based on both the parameters and the spatial coverage, and estimate the stable constraints by the cost function with a regulation term. Also, our plane merging technique provides a way of generating local maps that are useful for estimating loop closure constraints. We have performed real-world experiments at our lab environment. The results demonstrate the efficiency and robustness of the proposed algorithm.

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This paper tries to resolve long waiting time to find a matching person in player versus player mode of online sports games, such as baseball, soccer and basketball. In player versus player mode, game playing AI which is instead of player needs to be not just smart as human but also show variety to improve user experience against AI. Therefore a need to design game playing AI agents with diverse personalized styles rises. To this end, we propose a personalized game AI which encodes user style vectors and card style vectors with a general DNN, named UCSM-DNN. Extensive experiments show that UCSM-DNN shows improved performance in terms of personalized styles, which enrich user experiences. UCSM-DNN has already been integrated into popular mobile baseball game: MaguMagu 2021 as personalized game AI.

In this paper, we propose on-device voice command assistants for mobile games to increase user experiences even in hands-busy situations such as driving and cooking. Since most of the current mobile games cost large memory (e.g. more than 1GB memory), so it is necessary to reduce memory usage further to integrate voice commands systems on mobile clients. Therefore a need to design an on-device automatic speech recognition system that costs minimal memory and CPU resources rises. To this end, we apply cross layer parameter sharing to Conformer, named MONICA2 which results in lower memory usage for on-device speech recognition. MONICA2 reduces the number of parameters of deep neural network by 58%, with minimal recognition accuracy degradation measured in word error rate on Librispeech benchmark. As an on-device voice command user interface, MONICA2 costs only 12.8MB mobile memory and the average inference time for 3-seconds voice command is about 30ms, which is profiled in Samsung Galaxy S9. As far as we know, MONICA2 is the most memory efficient yet accurate on-device speech recognition which could be applied to various applications such as mobile games, IoT devices, etc.