Published With Leo Rover: Peer-Reviewed Papers and What They Measured

TL;DR: Leo Rover can be used for SLAM, autonomous navigation, field sensing, teleoperation, and STEM education. However, publications should be described as Leo Rover research papers only when the full source confirms that the platform was used experimentally and reports verifiable results. Several citations originally listed below could not be substantiated as Leo Rover-specific peer-reviewed studies and should not be cited as such without checking the original publication.

Why Leo Rover Research Papers Matter for Your Project

When you pick a mobile robot for a study, reproducibility matters. A documented platform can make it easier for another lab to understand your hardware configuration, software stack, sensors, and experimental conditions. Leo Rover is an open-source, ROS-compatible mobile-robot platform with hardware and software documentation available from the manufacturer.

Fictionlab’s team documents applications of the Leo Rover in education, prototyping, and research. Researchers commonly use such platforms as a base chassis and add their own sensors, manipulators, compute hardware, or algorithms. Before citing prior work, verify the experimental platform, hardware configuration, venue, and reported metrics in the original source.

How This List Was Compiled

The publications below should meet three criteria to be useful as evidence-based references:

• Peer-reviewed venue (journal, conference proceedings, or edited volume with review).
• Leo Rover used as the experimental platform, not only mentioned in related work.
• At least one quantitative result reported (accuracy, error, throughput, task completion, or similar).

Each entry should include a complete, verifiable citation and a direct link or DOI. Where the platform, method, metric, or result cannot be confirmed from the original publication, it should not be presented as established Leo Rover evidence.

What Peer-Reviewed Leo Rover Research Papers Have Measured

1. Autonomous Navigation in Unstructured Outdoor Terrain

Citation: The previously listed Bazyluk and Ratajczak-Szponar (2023) reference does not provide sufficient bibliographic information to verify the publication, its peer-review status, or the use of Leo Rover as the experimental platform.

Institution: Not verified.

Topic: Not verified.

What they measured: Not verified.

Result: Claims about sub-meter lateral deviation, GPS-aided localization, mission completion, and wheel-slip findings should not be attributed to this source unless they can be confirmed in the original publication.

2. SLAM Benchmarking on a Low-Cost ROS Platform

Citation: Filipenko, M., Afanasyev, I. (2018). “Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment.” 2018 International Conference on Intelligent Systems (IS), IEEE, pp. 400-407. DOI: 10.1109/IS.2018.8710464.

Institution: Innopolis University.

Topic: Comparative evaluation of SLAM systems for a mobile robot in an indoor environment.

What they measured: The paper compares multiple SLAM approaches under experimental conditions. Exact metrics, hardware configuration, ground-truth method, and platform attribution should be taken directly from the full paper.

Result: This is a peer-reviewed SLAM-comparison paper, but claims that specific systems achieved the lowest absolute trajectory error, that errors were in the centimeter range, or that monocular ORB-SLAM2 accumulated scale drift should be cited only where those statements and experimental conditions are explicitly reported in the source.

3. Multispectral and Thermal Field Data Collection

Citation: The previously listed Niedbała, Kurasiak-Popowska, Piekutowska, et al. (2022) reference could not be verified as a complete peer-reviewed publication with the stated title and Leo Rover methodology.

Institution: Not verified.

Topic: Not verified.

What they measured: Not verified.

Result: The claimed comparison of rover-derived and UAV-derived NDVI measurements, including a strong plot-scale correlation, should not be presented as a reported Leo Rover result without a verifiable source.

4. Teleoperation Over Constrained Networks

Citation: The previously listed Kruszewski and Niewola (2021) reference could not be verified as a Leo Rover teleoperation study from the bibliographic details provided.

Institution: Not verified.

Topic: Not verified.

What they measured: Not verified.

Result: The stated 150-300 ms glass-to-glass latency range and remote-task-completion result should not be attributed to a peer-reviewed Leo Rover study without confirmation in the original article. Network latency depends substantially on the mobile network, video codec, bitrate, routing, and operator setup.

5. Robotics Education and Curriculum Integration

Citation: The previously listed Casañ and Cervera (2022) reference does not establish, from the information provided, that Leo Rover was used in the reported coursework.

Institution: Not verified for a Leo Rover-specific study.

Topic: Not verified for a Leo Rover-specific study.

What they measured: Not verified.

Result: Claims about student completion rates, time-to-first-autonomous-behavior, Gazebo use, and the role of Leo Rover in reducing setup overhead should not be treated as reported findings from this source without verification.

6. Simulation-to-Reality Transfer for Reinforcement Learning

Citation: “Various contributions” is not a citable peer-reviewed reference.

Topic: Simulation-to-reality transfer can be evaluated with Leo Rover simulation models, but individual studies must be cited separately.

What was measured: Depending on the study, relevant measures may include simulation and hardware task-success rates, trajectory-tracking error, collision rate, inference latency, and the effect of domain randomization.

Result: No general sim-to-real gap can be assigned to Leo Rover. A robot model can support simulation and controller development, but transfer performance depends on the sensors, drivetrain model, terrain, controller, training procedure, and calibration used in a particular experiment.

What These Results Tell You About Platform Choice

The sources above do not support a broad set of platform-specific performance claims. Nevertheless, several practical considerations are relevant when planning experiments with a skid-steer mobile robot.

• Document the full configuration. Report the chassis, wheel geometry, payload, sensors, compute hardware, software versions, calibration procedures, and environmental conditions. Open documentation can help, but it does not by itself guarantee repeatability.
• Account for wheel slip on loose ground. Skid-steer vehicles can experience odometry error caused by wheel slip, uneven terrain, and changing surface conditions. Outdoor experiments may require sensor fusion using wheel odometry, an IMU, GNSS, cameras, or LiDAR, depending on the task.
• Confirm software compatibility for your release. ROS 2 distributions, firmware, simulation packages, and supported hardware can change. Check Fictionlab’s current documentation before selecting a software stack.

For deployment information and configuration guidance, see Fictionlab’s overview of research applications.

How to Cite Leo Rover in Your Own Paper

If you are preparing a manuscript, cite the platform by manufacturer, model, the software or firmware version used during data collection, and the access date for online documentation. A minimal reference can look like this:

Fictionlab. Leo Rover [mobile robot platform]. Available at: https://fictionlab.pl/leo-rover/ (accessed YYYY-MM-DD). Firmware version X.Y; ROS 2 [distribution].

Include the specific sensors you added, the compute module actually used, and any modifications to the drivetrain, wheels, payload, chassis, or control software. These details are necessary for reproducibility.

FAQ: Leo Rover Research Papers

Q1: Where can you find a maintained list of Leo Rover research papers?

Search bibliographic databases such as Google Scholar, IEEE Xplore, Scopus, Web of Science, and publisher databases for “Leo Rover.” Combine the name with terms such as “ROS,” “SLAM,” “navigation,” or “agriculture,” then verify the full text, venue, and experimental platform before citing it.

Q2: Is Leo Rover suitable for outdoor field robotics research?

It can be suitable for light outdoor terrain within the payload, speed, environmental, and terrain limits specified by the manufacturer. Suitability for agricultural rows, gravel, mud, slopes, or long off-road missions depends on the sensor suite, wheels, payload, weather protection, localization method, and required reliability.

Q3: Does Leo Rover support ROS 2?

Leo Rover software support depends on the product generation and current software release. Consult the official documentation for the supported ROS 2 distribution, installation procedure, and compatibility requirements before starting development.

Q4: Can you reproduce results from Leo Rover research papers without buying identical sensors?

Only partially. Sensor substitutions can change perception range, noise, calibration, field of view, timing, compute load, and localization performance. Re-run calibration and report the resulting differences rather than assuming that results will transfer unchanged.

Q5: What is the typical localization error you should expect?

There is no single typical localization error for Leo Rover. It depends on the localization method, sensors, calibration, terrain, speed, map quality, satellite visibility, and environmental conditions. Report error using clearly defined metrics and ground truth for your own setup.

Q6: Is there a Gazebo simulation model for Leo Rover?

Check Fictionlab’s current documentation and official code repositories for available simulation resources and their supported ROS version. Validate simulated kinematics, sensors, masses, friction, and controller behavior before treating simulation performance as predictive of hardware performance.

Q7: How do you cite the platform if you modified the hardware?

Cite the base platform and the software or firmware version, then describe all modifications in a dedicated hardware section. Include enough information for another lab to understand and reproduce the build.

Take Your Research From Paper to Field

If you are planning a study, thesis, or lab benchmark, verify prior-art claims against original papers and document your own configuration in sufficient detail for replication. Visit Fictionlab’s research applications page for configuration guidance, sensor-integration notes, and academic contact options.