Accurate location estimation for last-mile delivery robot - Cheong, Jia Jun (Nanyang Technological University, 2023)
E-commerce has been steadily growing in demand over the past few years. Coupled with the global pandemic keeping everyone in their homes, the demand for e-commerce has never been stronger. As a result, many delivery companies have been looking for ways to improve and streamline their delivery service. Particularly challenging and costly is the final leg of delivery, known as the last-mile delivery. To tackle this challenge, one solution being explored involves the utilization of delivery robots. Delivery robots would help to drastically reduce manpower required for delivery services, allowing for an autonomous delivery experience. Highly precise and reliable self-positioning mechanism is essential for the robot to accurately navigate through their surroundings. This study introduces the concept of SLAM (Simultaneous Localization and Mapping) as an innovative approach in robotics and computer vision. The aim is to tackle the intricate task of mapping an unfamiliar environment while concurrently determining the precise location of the robot within that environment. We will implement GVINS, an autonomous system that provides real-time localization, on a delivery robot, and its efficacy is evaluated through a range of datasets.