A benchmark analysis of data-driven and geometric approaches for robot ego-motion estimation

In the last decades, ego-motion estimation or visual odometry (VO) has received a considerable amount of attention from the robotic research community, mainly due to its central importance in achieving robust localization and, as a consequence, autonomy. Different solutions have been explored, leading to a wide variety of approaches, mostly grounded on geometric methodologies and, more recently, on data-driven paradigms. To guide researchers and practitioners in choosing the best VO method, different benchmark studies have been published. However, the majority of them compare only a small subset of the most popular approaches and, usually, on specific data sets or configurations. In contrast, in this work, we aim to provide a complete and thorough study of the most popular and best-performing geometric and data-driven solutions for VO. In our investigation, we considered several scenarios and environments, comparing the estimation accuracies and the role of the hyper-parameters of the approaches selected, and analyzing the computational resources they require. Experiments and tests are performed on different data sets (both publicly available and self-collected) and two different computational boards. The experimental results show pros and cons of the tested approaches under different perspectives. The geometric simultaneous localization and mapping methods are confirmed to be the best performing, while data-driven approaches show robustness with respect to nonideal conditions present in more challenging scenarios.

Automated summary

In the field of robotics research, visual odometry (VO), which estimates ego-motion, has become increasingly important for achieving robust localization and autonomy. Various solutions based on geometric and data-driven approaches have been explored, but there is a lack of comprehensive benchmark studies comparing these methods. In this work, a thorough study of the most popular and best-performing geometric and data-driven solutions for VO is provided, considering different scenarios and environments, estimation accuracies, hyper-parameters, and computational resources. Experimental results on diverse datasets and computational boards reveal the pros and cons of the tested approaches, with geometric simultaneous localization and mapping methods performing best and data-driven approaches showing robustness in challenging scenarios.