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Abstract. In recent years, wearable devices such as smart bands and smartwatches have gained widespread popularity due to their ability to provide various health and fitness applications by detecting and analyzing the human body and motion information. However, the accuracy of location-based services can be limited, especially in urban areas and indoors. This study proposes a series of smartwatch Pedestrian Dead Reckoning (PDR) improvements based on 9 Degrees of Freedom (DOF) IMU orientation estimation, which includes the heading estimation of human movement and a novel pre-trained velocity regression model. The proposed system holds the potential to enhance positioning accuracy and augment navigation availability for smartwatch users, thus offering potential applications across various fields. This study makes significant contributions to the field of smartwatch navigation by proposing a GNSS/PDR fusion algorithm specifically designed for the consumer-grade IMU, magnetometer, and GNSS receiver built into Apple Watch, tracking varied roll and pitch of the sensor caused by hand swing, and integrating a CNN model to predict the 1-D speed and provide ZUPT information, offering improved accuracy and reliability.

Abstract. One of the most popular research areas is low-cost navigation and positioning systems for autonomous vehicles. Determining a vehicle's position within a lane is critical for achieving high automation. Vehicle navigation and positioning relied heavily on the Global Navigation Satellite System (GNSS) service in open-sky scenarios. Nonetheless, GNSS signals were easily degraded due to various environmental situations such as urban canyons caused by multi-path effects and Non-Line-of-Sight (NLOS) issues. To perform robustly in complex scenarios, sensor fusion is the most common solution. The following paper presents a radar visual odometry framework to improve the lack of scale factors for monocular cameras and poor angular resolution for radar. The framework is based on the characteristics of camera and radar sensors which have complementary advantages in each other. The results show that the proposed framework can be used to estimate general 2D motion in an indoor environment and correct the unknown scale factor of Monocular Visual Odometry in a real-world setting.