An Assessment of the Use of Visual Odometry in Localization

Localization is the process of determining the path, position and orientation of a robot in an environment. The data from different sensors such as LiDAR, inertial measurement units (IMU) or cameras are used in the localization process. This task is of fundamental importance to enable robots to navigate autonomously and perform tasks effectively. The robot localization can be performed utilizing different techniques either using hardware or software designs. Visual localization algorithms can be shown as one of the localization techniques that enable robots to determine their position and orientation. In this aspect, visual odometry is one of the mostly used methods. It is a technique that enables robots to determine their positions and movements by analysing sequential images. It tracks features in consecutive images to determine the movement of the camera between those frames which enables the determination of the robot’s position in the environment. The process commonly involves detecting and matching key points or features in the images, such as corners or edges, and then using algorithms to calculate the camera's motion. Visual odometry is useful in environments where Global Navigation Satellite Systems (GNSS) or other external positioning systems cannot operate. In this study, the use of visual odometry is assessed in comparison with the iPad Pro LiDAR and steel tape results to determine the distances between each image-taking point. The iPad and steel tape results were taken as the ground truth and the root mean square values were determined by comparing the algorithm and their results.