Visual Odometry (VO) technique for challenging environments with focus on low-texture

Master thesis project, entitled “Visual Odometry Technique for challenging environment with focus on low-texture”, has been carried out at PIC4SeR (Politecnico di Torino Interdepartmental Centre for Service Robotics). Service robotics is based on creating autonomously or semi-autonomously systems useful for the human wellbeing. It can have several applications ranging from precision agriculture to space and to services for people with disabilities. The latter is the field of this research project: visual sensor is mounted on a wheelchair that is moved in indoor environment. Different cameras and Visual Odometry algorithms are evaluated in order to obtain a precise real-time position variation, focusing on low-texture challenge. Generally, indoor environments, such as hospital, do not have many features, so placing a front-facing camera may not solve low-texture problems. The idea of placing the visual sensor facing downwards as well as frontally is proposed, in order to focus exclusively on the floor texture and obtain the desired Odometry with higher accuracy. The purpose of this research is to localize a robot within limited indoor environment through Visual Odometry. The algorithm discussed in this Master thesis project consists of two parts. In the first one, consecutive images captured by the camera are considered. Thus, landmarks in the surrounding environment are examined and feature detection and matching algorithms are applied, evaluating the change in position of that feature between one frame and the following. In the second part, the Essential Matrix and the Fundamental Matrix are calculated, obtaining an estimate of the robot's position point by point. At the end of the algorithm, the trajectory of the wheelchair is obtained. After having created a simulation environment on Gazebo and once transcribed the above-mentioned algorithms in Python language, ROS 2 Foxy is used to simulate the behaviour of the system. The correctness of the robot's trajectory is verified by comparing the Odometry obtained from Gazebo and the Odometry obtained through the algorithm, by means of a Jackal 3D model. Afterwards, once evaluated the efficiency of the algorithms, system is simulated in real world using a Turtlebot3 Burger robot. Through this experimental approach, it can be demonstrated that a low-texture environment compromises the accuracy of the trajectory; however, with the camera facing downwards, features are greater and the odometry is more accurate.