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

Abstract

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.

ARD‐VO: Agricultural robot data set of vineyards and olive groves

“…This section discusses the results obtained comparing different pose estimation algorithms. For this purpose, we selected a set of approaches among the most popular for both vision-based (see Legittimo et al, 2023) and LIDAR-based (see Huang et al, 2022;Jonnavithula et al, 2021) pose estimation. In particular, we considered:…”

“…This section discusses the results obtained comparing different pose estimation algorithms. For this purpose, we selected a set of approaches among the most popular for both vision‐based (see Legittimo et al, 2023) and LIDAR‐based (see Huang et al, 2022; Jonnavithula et al, 2021) pose estimation. In particular, we considered: Direct Sparse Odometry (DSO) (Engel et al, 2017) and ORB‐SLAM2 Mono (Mur‐Artal & Tardós, 2017) for monocular camera setups; VINS‐Mono (Qin et al, 2018) for monocular‐inertial setups; ORB‐SLAM2 Stereo (Mur‐Artal & Tardós, 2017) for stereo‐camera setups; Open‐VINS (Geneva et al, 2020) for stereo‐inertial setups; FLOAM (Wang et al, 2020) and LeGO‐LOAM (Shan & Englot, 2018) for LIDAR‐based setups.We performed comparative experiments on a selection of eight sequences, considering two sequences for each cultivation field.…”