Testing the real-time performance of a monocular visual odometry method for a wheeled robot

Navigating robots with precision and efficiency is a fundamental challenge in the field of robotics. Central to this challenge is the critical aspect of odometry, the ability to estimate a robot’s motion relative to its environment. In this context, this paper presents an evaluation of the generalizability and effectiveness of a monocular visual odometry method in the context of navigation on a wheeled robot. The study aims to assess TartanVO’s performance in real-time motion estimation and its ability to handle various challenges encountered in indoor and outdoor environments. For this purpose, we designed our methodology framework to evaluate the real-time effectiveness of the TartanVO method by utilizing data streams from a robot’s on-board sensors. To validate the performance of TartanVO, we compared its pose estimations against ZED pose estimations, analyzing the mean absolute error of the trajectories produced by each method. We collected time-synchronized data from both TartanVO and ZED positional estimate methods, enabling simultaneous position estimation from both methods. Experimental results reveal that the TartanVO method demonstrates impressive real-time efficiency and generalizability, positioning it as a promising solution for odometry in robots operating in various environments. However, challenges were identified, including scale drift and suboptimal pose estimation in low-light conditions and open outdoor areas when tested with the Jackal robot. These findings underscore the need for further refinement in addressing specific environmental nuances, while acknowledging the overall potential of the method in real-time motion estimation.