Visual Path Odometry for Smart Autonomous E-Bikes

Recently, autonomous systems have been in rapid development. These systems are composed of various modules that enable automated navigation. The main generic modules consist of data acquisition, perceiving the environment, finding the optimal path and controlling the actuators. A core module of the automated vehicles is estimating where the vehicle is with respect to the path to be traversed, namely, vehicle localization. Localization has been applied through the past few years based on various sensors such as LiDARs, Cameras and GPS. Each sensor has its own strengths and weaknesses. For instance, Camera and LiDAR-Based traversed path estimation Odometry can function within GPS-denied environments. On the other hand, localization based on GPS can be used in environments where Cameras and LiDARs can fail to extract useful information, such as in a desert. For the case of automated vehicles, there is access to high power computers on board, which can help run complex and computationally expensive algorithms. However, for smaller platforms such as smart bikes, they are computationally limited, which can be a challenge especially for Visual Odometry Based algorithms. Given that there have been few researches exploring Monocular Visual Odometry (MVO) algorithms that are computationally efficient, this paper proposes the use of and enhancing a MVO module to estimate the path traversed by a smart bike platform in real-life. The obtained scores were compared to the output of the tested algorithm on a consumer grade PC to explore the trade-off between the gained speed up and reduced accuracy, the to validate the results.