Comparison of ROS-Based Monocular Visual SLAM Methods: DSO, LDSO, ORB-SLAM2 and DynaSLAM

Mingachev, Eldar; Lavrenov, Roman; Tsoy, Tatyana; Matsuno, Fumitoshi; Suthakorn, Jackrit; Magid, Evgeni

doi:10.1007/978-3-030-60337-3_22

Cited by 30 publications

(18 citation statements)

References 22 publications

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“…More challenging contexts are considered by Mingachev et al (2020), where DSO (Engel et al, 2018), LDSO (Gao et al, 2018), ORB‐SLAM2 (Mur‐Artal & Tardós, 2017a), and DynaSLAM (Bescos et al, 2018) are tested on the EuRoC (Burri et al, 2016) and the TUM (Schubert et al, 2018) data sets, collected with a MAV and a hand‐held camera, respectively.…”

Section: Related Workmentioning

confidence: 99%

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

Legittimo

Felicioni

Bagni

et al. 2023

Journal of Field Robotics

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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.

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Section: Related Workmentioning

confidence: 99%

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

Legittimo

Felicioni

Bagni

et al. 2023

Journal of Field Robotics

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“…The data include outdoor scenes with some vegetation in a university campus but is mainly composed of indoor and urban environments. In Mingachev et al (2020), monocular visual SLAM methods are compared using TUM VI and EuRoC data sets.…”

Section: Related Workmentioning

confidence: 99%

Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

Cremona

Comelli

Pire

2022

Journal of Field Robotics

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The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (SLAM) systems, key for mobile robotics, given the visual difficulty due to the highly repetitive scene and the crop leaves movement caused by the wind. In recent years, several Visual-Inertial Odometry (VIO) and SLAM systems have been developed.They have proved to be robust and capable of achieving high accuracy in indoor and outdoor urban environments. However, they were not properly assessed in agricultural fields. In this study we assess the most relevant state-of-the-art VIO systems in terms of accuracy and processing time on arable lands to better understand how they behave on these environments. In particular, the evaluation is carried out on a collection of sensor data recorded by our wheeled robot in a soybean field, which was publicly released as the Rosario data set. The evaluation shows that the highly repetitive appearance of the environment, the strong vibration produced by the rough terrain and the movement of the leaves caused by the wind, expose the limitations of the current state-of-the-art VIO and SLAM systems. We analyze the systems failures and highlight the observed drawbacks, including initialization failures, tracking loss and sensitivity to Inertial Measurement Unit saturation. Finally, we conclude that even though certain systems like ORB-SLAM3 and stereo Multi-State Constraint Kalman Filter show good results with respect to others, more improvements should be done to make them reliable in agricultural fields for certain applications such as soil tillage of crop rows and pesticide spraying.

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“…As both feature-based and direct methods have their pros and cons according to different environment circumstances and purposes [38], it can be concluded that it is important to take a feature-based method into account as well as a direct method to assess their performances in indoor agricultural environments.…”

Section: Visual Slam Algorithmsmentioning

confidence: 99%

“…In short, this indicates that one run of the multiple runs performed within an algorithm has been taken as reference and the other runs will be scaled toward the same scale factor. As in [38], the references were obtained directly from the VSLAM algorithms. Scaling the runs with each other was necessary; otherwise, the error due to the differences in scale would also be taken into account and that was not desired.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

Krul

Pantos

Frangulea

et al. 2021

Drones

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Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

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Comparison of ROS-Based Monocular Visual SLAM Methods: DSO, LDSO, ORB-SLAM2 and DynaSLAM

Cited by 30 publications

References 22 publications

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

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

Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

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