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REVIEW 4 major objections 6 minor 33 references

Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Fractal dimension of generated terrain cuts robot mission success by 25%

desk verdict A useful tool paper that extends 1D W-M terrain profiles to 2D DEMs; the central trend is plausible but needs uncertainty quantification. read the letter →

arxiv 2501.02172 v1 pith:UQ3BSLIU submitted 2025-01-04 cs.RO

Casey D. Majhor , Jeremy P. Bos This is my paper · ORCID
classification cs.RO
keywords multifractalterraingenerationWeierstrass-Mandelbrotfunctionfractaldimensionroughnessautonomousgroundvehicleoff-roadnavigationsimulationgradientmap
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a terrain generation method that combines three Weierstrass-Mandelbrot surfaces at different spatial frequencies into a single multifractal digital elevation map, and claims that varying the fractal dimension $D$ of the high-frequency layer is enough to control terrain roughness. Over 60 generated terrains, raising $D$ from 2.3 to 2.45 to 2.6 shifts the median composition from low-roughness toward semi- and high-roughness areas, and a simulated autonomous ground vehicle's median success rate on straight-line missions falls by 22.5% and 25% respectively across the same increments. The payoff is that terrain difficulty becomes a tunable input for off-road vehicle simulations, rather than an uncontrolled property of a pre-built map. If correct, researchers can stress-test path planners with a single scalar dial.

What carries the argument

The central object is the three-dimensional Weierstrass-Mandelbrot (W-M) function, a sum of ridge-like sinusoids whose amplitudes are governed by the fractal dimension $D$. The paper combines three W-M surfaces of different spatial frequency (ridge counts $M = 16, 32, 64$) by pixel-wise multiplication into one multifractal digital elevation map, then varies $D$ only in the high-frequency surface. Terrain roughness is quantified by gradient maps: a Moore-neighborhood maximum gradient is computed at every pixel, with thresholds of 50 and 140 rise-over-run separating low-, semi-, and high-roughness classes. These gradient maps both drive mission start/goal selection and serve as the roughness metric that correlates with vehicle performance.

What would settle it

Recalibrate the 50 and 140 gradient thresholds on separately generated terrains with $D=2.3$ and $D=2.6$; if the thresholds that separate navigable from non-navigable terrain shift substantially, then the reported roughness-composition percentages and their correlation with success rate depend on calibration choices rather than on $D$ alone.

Watch

Extended reading notes

Core claim

The central claim is that the fractal dimension $D$ of the high-frequency Weierstrass-Mandelbrot component is a control parameter for the difficulty of off-road terrain. Using 20 random digital elevation maps per $D$ value, the authors find that increasing $D$ from 2.3 to 2.45 to 2.6 decreases the median low-roughness area by 13.8% and 7.16%, increases the median semi-rough area by 11.7% and 5.63%, and increases the median high-roughness area by 1.54% and 3.33%. In 1,200 simulated straight-line missions, the median success rate of the autonomous ground vehicle falls by 22.5% and 25% across the same increments, and successful traversals show higher median RMS vertical acceleration and pitch and roll rates. The paper interprets this as evidence that roughness can be dialed in by a single scalar, making the terrain generator a useful tool for off-road vehicle evaluation.

Load-bearing premise

The roughness categories rest on gradient thresholds (50 and 140) that were chosen by visually judging one vehicle's navigability on a single $D=2.45$ terrain, yet they are applied to all 60 terrains and all three $D$ values as though they were an objective, vehicle-independent measure of difficulty.

Editorial extensions

If this is right

  • Terrain difficulty for simulated off-road tests can be selected in advance by choosing $D$, allowing controlled stress-testing of path planners and navigation algorithms.
  • A terrain-aware path planner should recover a large share of the success-rate loss on higher-$D$ terrains, since the straight-line planner used here ignores roughness entirely.
  • The generated digital elevation maps are standard raster files, so the same roughness control transfers to other robotics simulators without modifying the generator.
  • The monotonic relationship between $D$ and both roughness composition and vehicle dynamics gives a quantitative scale for reporting terrain difficulty across experiments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension of the paper is to treat the gradient thresholds as tunable parameters rather than fixed constants; fitting them per vehicle would separate terrain difficulty from vehicle capability.
  • The same generator could be used to benchmark terrain-aware planners head-to-head: fixing $D$ while varying the planner isolates the planner's contribution to mission success on matched terrain.
  • Since only $D$ in the high-frequency layer was varied, the method likely supports a second control axis (e.g., varying $D$ in the mid-frequency layer) to produce terrains with the same overall roughness fraction but different spatial patchiness, which would test whether planners fail due to roughness magnitude or roughness distribution.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes a multifractal terrain generation method based on the 3D Weierstrass-Mandelbrot function, combining low-, mid-, and high-frequency DEMs and varying only the fractal dimension D of the high-frequency component across 2.3, 2.45, and 2.6. For each D, 20 unique 50.4 m × 50.4 m DEMs are generated, rendered in Unreal Engine, classified into low/semi/high roughness areas using Moore-neighborhood gradient thresholds (50 and 140), and traversed by a simulated Clearpath Husky over 20 randomized straight-line A-to-B missions at 1 m/s. The authors report that increasing D decreases the median low-roughness area by 13.8% and 7.16%, increases the median semi-rough area by 11.7% and 5.63%, increases the median high-roughness area by 1.54% and 3.33%, and decreases the median AGV success rate by 22.5% and 25%. They also report that RMS vertical acceleration, pitch/roll rates, and traversal times increase with D for successful trials. The central claim is that terrain difficulty can be controlled by a single scalar parameter.

Significance. If the central claim holds, the method is a useful, low-cost tool for generating varied off-road terrain for AGV simulation, and it extends prior W-M profile work to full 3D DEMs suitable for path planning and navigation tests. The direct measurement of success rates across randomized missions is a sensible and falsifiable protocol, and the DEM output is portable across simulators. The paper honestly notes that results are specific to the modeled Husky and simulator. However, the strength of the quantitative claims is currently limited by the absence of statistical inference (no confidence intervals, bootstrap, or hypothesis tests around the median differences), by roughness thresholds and UE terrain scaling calibrated on the same simulated vehicle and D=2.45 terrain, and by incomplete reporting of several generation parameters. These issues are fixable and do not invalidate the qualitative direction of the results.

major comments (4)
  1. [§3.1, §3.2, Figs. 5 and 6] The headline changes in median low-roughness area and success rate are point estimates over n = 20 terrain maps per D group, reported without confidence intervals, bootstrap intervals, or hypothesis tests. Each per-map success rate is a proportion of 20 trials, so it is quantized at 5% increments, and the boxplots in Figure 6 show considerable overlap between groups. Before claiming that D 'dials in' difficulty, the authors should show that the median differences (13.8%, 7.16%, 22.5%, 25%) exceed sampling noise, for example with a permutation test or bootstrap confidence intervals on the median differences.
  2. [§2.2, §2.3, §3.1] The gradient thresholds of 50 and 140 and the UE terrain Z-scaling were calibrated by driving the same simulated Husky on D=2.45 terrain and visually judging navigability, and then applied to all D values as if they were objective, vehicle-independent measures. Consequently, the roughness-composition percentages in Section 3.1 are partly an artifact of the calibration procedure rather than an independent measure of difficulty. The authors should either provide evidence that the thresholds transfer across D values and vehicles, perform a sensitivity analysis over reasonable threshold values, or explicitly frame the composition results as conditional on these calibration choices. The direct success-rate comparison is less affected by this issue.
  3. [§3.2, Fig. 7] The dynamics metrics (RMS vertical acceleration, RMS pitch/roll rates, traversal times) are computed only on successful trials, with 361, 282, and 225 successful trials for D=2.3, 2.45, and 2.6. Because success rates differ systematically by group, these subsets are not directly comparable random samples, and the reported median increases could reflect selection effects. The authors should test whether the differences persist when accounting for the unequal and selected samples, or at minimum report trial-level analyses with appropriate clustering by terrain map.
  4. [§2.1–§2.3] Several parameters required to reproduce the generation method are not specified, including the Gaussian low-pass filter parameters, the exact DEM rescaling procedure, and the morphological closing disk size. Since the paper's contribution is a terrain generation method, omitting these parameters limits reproducibility and prevents readers from assessing how sensitive the composition results are to these choices.
minor comments (6)
  1. [§2.4] 'Inner-quartile range' should be 'interquartile range'; the definition in Eq. (6) is correct but the term used in the text is not.
  2. [Fig. 4 caption] The caption states that high-roughness areas cannot contain a start/goal location, but Section 2.3 allows goal locations in semi-rough areas and only restricts start locations to low-roughness areas; the caption should be corrected to match the text.
  3. [Eq. (1)] The notation in Eq. (1), particularly the factor multiplying the cosine argument, is difficult to parse; please clarify or simplify the expression and ensure all symbols are defined.
  4. [References] Reference [2] lists an access date of February 30, 2023, which is not a valid date; please correct the access date or the URL.
  5. [Data Availability] The data availability statement says data are 'obtainable from the corresponding author upon reasonable request'; for a methods paper, providing the DEMs, parameter files, or code in a public repository would strengthen reproducibility.
  6. [Fig. 5] The bar graph in Figure 5 shows no measure of variability; given n=20 per group, adding IQR whiskers or overlaid points would make the between-group comparison more informative and consistent with Figure 6.

Circularity Check

1 steps flagged · score 2.0 of 10

Roughness-category explanation is partly tautological, but the central D-to-success-rate relation is measured directly and is not circular.

  1. self definitional [Sec. 2.3 (threshold calibration) and Sec. 3.2 (success-rate explanation)]
    "These category thresholds were determined by deploying the AGV and traversing UE terrain locations corresponding to the DEM gradient map. We found low-roughness terrain to be smooth and easily navigable. Terrain areas classified as semi-rough were moderately rough visually, but less navigable than low-roughness terrain. We found high-roughness areas very rough visually and likely to cause the AGV to become stuck or rollover. [...] This is explained by the increase in the median semi- and high-roughness terrain areas and the decrease in the median low-roughness areas."

    The low-, semi-, and high-roughness categories are not independent, objective terrain descriptors: their thresholds (50 and 140 rise-over-run) were selected by observing the same simulated Husky AGV and labeling areas that were 'smooth and easily navigable' versus 'likely to cause the AGV to become stuck or rollover.' The paper then uses these categories to explain the measured success-rate decrease, saying success drops because there is more semi/high-roughness area and less low-roughness area. That explanatory link is partly an unpacking of the classifier's own definition rather than an independent causal finding.

full rationale

The central empirical claim, that increasing the high-frequency DEM fractal dimension D from 2.3 to 2.45 to 2.6 reduces median AGV success rates by 22.5% and 25%, is a direct measurement from 1,200 simulated trials and is not derived from or fitted to the roughness classifier. Similarly, the reported 13.8%/7.16% decreases in median low-roughness area are direct statistics computed from fixed gradient thresholds applied to 60 DEMs. The only circularity is interpretive: the roughness categories were calibrated by the same simulated Husky's navigability, and Sec. 3.2's statement that the success-rate drop is 'explained by' the increase in semi/high-roughness area partially restates that calibration. This does not undermine the direct D-to-success trend, which would stand even if the thresholds were different, though the absolute percentages and the roughness-composition framing would change. The absence of confidence intervals or significance tests is a statistical robustness concern, not a circularity, and does not raise the circularity score.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claims rest mainly on domain assumptions about simulator fidelity and on hand-tuned parameters (Z-scale, gradient thresholds, Gaussian filter, DEM rescaling). No new physical entities are introduced. The 'multifractal' label is applied to a product of three monofractal W-M surfaces, but the paper does not verify multifractal properties such as a spectrum of local scaling exponents.

free parameters (6)
  • Unreal Engine terrain Z scale = 0.75%
    Chosen by experimenting with D=2.45 so the terrain has both smooth and immobilizing sections (Sec. 2.2). It sets the vertical exaggeration for all 60 terrains and directly affects absolute roughness and success rates.
  • Gradient roughness thresholds = 50 and 140 (rise-over-run)
    Determined by deploying the simulated AGV on a D=2.45 terrain and visually assessing navigability (Sec. 2.3). These thresholds define the low/semi/high roughness categories used in the terrain-composition results.
  • Gaussian low-pass filter parameters = not specified
    Each monofractal DEM is smoothed with an unspecified Gaussian filter (Sec. 2.1); the kernel size affects gradient maps and roughness classification.
  • DEM rescaling procedure = not specified
    The three DEMs are 'scaled near the same range and with zero mean' before pixel-wise multiplication (Sec. 2.1); the exact normalization is not given and affects the final amplitude distribution.
  • Morphological closing disk size = not specified
    A morphological closing filter with a disk structuring element is applied to gradient maps for mission selection (Sec. 2.3); the disk radius is not stated.
  • W-M function parameters (M, gamma, L, G) = M=16/32/64, gamma=1.5, L=100.9, G=1e-6/8e-8/1e-8
    Taken from prior work by Dawkins et al. (Refs. 11-13) and Table 1; these define the base surfaces. They are inputs, not fitted to outcomes, but the D-dependence conclusion is only demonstrated for this parameter set.
assumptions (6)
  • standard math The 3D Weierstrass-Mandelbrot function generates surfaces with the prescribed fractal dimension and statistically homogeneous properties (Ref. 14).
    Invoked in Sec. 2.1 to justify using the W-M function for terrain generation and to bound the D sampling range.
  • domain assumption NVIDIA PhysX accurately captures vehicle-terrain interaction for the simulated Husky (Ref. 27).
    The paper relies on PhysX to produce realistic success rates and vehicle dynamics in UE; cited in Sec. 2.2.
  • domain assumption The simulated Clearpath Husky and the straight-line pure-pursuit tracker are representative enough that traversal success and dynamics reflect terrain difficulty.
    The conclusions about terrain difficulty rest on this specific vehicle and controller; the paper itself notes results are specific to the Husky.
  • ad hoc to paper The gradient thresholds calibrated on a D=2.45 terrain generalize to D=2.3 and D=2.6 terrains.
    Sec. 2.3 applies the same 50/140 thresholds to all DEMs without re-calibration or validation across different D values.
  • domain assumption Twenty random seeds per D value are sufficient for medians to represent the terrain generation process.
    The paper uses 20 unique DEMs per D and 20 trials per DEM but provides no statistical test or power analysis; variance across seeds is shown via boxplots only.
  • domain assumption The failure criteria (pitch/roll beyond +/-75 degrees; displacement less than 0.2 m in 30 s) correctly identify mission failure.
    Defined in Sec. 2.4; these thresholds determine the success-rate metric that forms the core result.

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Cite this review

Pith. "Pith review of Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles." pith.science (2026). https://pith.science/paper/UQ3BSLIU

@misc{pith2026250102172,
  author       = {Pith},
  title        = {Pith review of: Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UQ3BSLIU}},
  note         = {Machine review of arXiv:2501.02172}
}
read the original abstract

We present a multifractal artificial terrain generation method that uses the 3D Weierstrass-Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of each terrain, consisting of low-, semi-, and high-roughness areas. To test how the fractal dimension affects the difficulty of vehicle traversals, we measure the success rates, vertical accelerations, pitch and roll rates, and traversal times of an autonomous ground vehicle traversing 20 randomized straight-line paths in each terrain. As we increase the fractal dimension from 2.3 to 2.45 and from 2.45 to 2.6, we find that the median area of low-roughness terrain decreases 13.8% and 7.16%, the median area of semi-rough terrain increases 11.7% and 5.63%, and the median area of high-roughness terrain increases 1.54% and 3.33%, all respectively. We find that the median success rate of the vehicle decreases 22.5% and 25% as the fractal dimension increases from 2.3 to 2.45 and from 2.45 to 2.6, respectively. Successful traversal results show that the median root-mean-squared vertical accelerations, median root-mean-squared pitch and roll rates, and median traversal times all increase with the fractal dimension.

Figures

Figures reproduced from arXiv: 2501.02172 by the authors.

Figure 1
Figure 1. Top and perspective views of three example monofractal terrains (a, c), (b, f), (c, g) generated using the 3D Weierstrass-Mandelbrot [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example low- (a), mid- (b), and high-frequency (c) monofractal digital elevation maps (DEMs), combined via the pixel-wise product [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. An example multifractal digital elevation map (DEM) with a top-down view of its grayscale image (a). The DEM in (a) is imported, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: An example Moore-neighborhood-gradient map with pixels categorized into one of three groups: low (blue), semi (orange), or high [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: A bar graph of the multifractal digital elevation maps’ (DEM) median areas of low (a), semi (b), and high roughness (c), all by the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Boxplots of the autonomous ground vehicle’s (AGV’s) success rate statistics in all terrains. The boxplots are grouped by the fractal [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Boxplots of the autonomous ground vehicle’s root-mean-squared (RMS) vertical accelerations (a), RMS pitch rates (b), RMS roll [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [1]

    Challenges in low infrastructure and off-road automated driving

    Carruth, D. W., Walden, C. T., Goodin, C., and Fuller, S. C., 2022. “Challenges in low infrastructure and off-road automated driving”. In Fifth International Conference on Connected and Autonomous Driving (MetroCAD), IEEE, pp. 13–20

    work page 2022

  2. [2]

    Grand challenge final report

    Defense Advanced Research Projects Agency, 2004. Grand challenge final report. Accessed February 30, 2023, from https://www.esd.whs.mil/Portals/54/Documents/FOID/Reading%20Room/DARPA/ 15-F-0059_GC_2004_FINAL_RPT_7-30-2004.pdf

    work page 2004

  3. [3]

    Stanley: The robot that won the darpa grand challenge

    Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoff- mann, G., et al., 2006. “Stanley: The robot that won the darpa grand challenge”. Journal of Field Robotics, 23(9), pp. 661–692

    work page 2006

  4. [4]

    A survey on procedural modelling for virtual worlds

    Smelik, R. M., Tutenel, T., Bidarra, R., and Benes, B., 2014. “A survey on procedural modelling for virtual worlds”. In Computer Graphics Forum, V ol. 33, Wiley Online Library, pp. 31–50

    work page 2014

  5. [5]

    Stochastic models for the earth’s relief, the shape and the fractal dimension of the coastlines, and the number-area rule for islands

    Mandelbrot, B. B., 1975. “Stochastic models for the earth’s relief, the shape and the fractal dimension of the coastlines, and the number-area rule for islands”. Proceedings of the National Academy of Sciences, 72(10), pp. 3825–3828

    work page 1975

  6. [6]

    Procedural modelling of terrains with constraints

    Gasch, C., Chover, M., Remolar, I., and Rebollo, C., 2020. “Procedural modelling of terrains with constraints”. Multimedia Tools and Applications, 79, pp. 31125–31146

    work page 2020

  7. [7]

    Controlled procedural terrain generation using software agents

    Doran, J., and Parberry, I., 2010. “Controlled procedural terrain generation using software agents”. IEEE Transactions on Computational Intelligence and AI in Games, 2(2), pp. 111–119

    work page 2010

  8. [8]

    Terrain generation using procedural models based on hydrology

    G ´enevaux, J.-D., Galin, ´E., Gu´erin, E., Peytavie, A., and Benes, B., 2013. “Terrain generation using procedural models based on hydrology”. ACM Transactions on Graphics (TOG),32(4), pp. 1–13

    work page 2013

Show all 33 references
  1. [9]

    Terrain synthesis from digital elevation models

    Zhou, H., Sun, J., Turk, G., and Rehg, J. M., 2007. “Terrain synthesis from digital elevation models”.IEEE Transactions on Visualization and Computer Graphics, 13(4), pp. 834–848

    2007

  2. [10]

    Authoring multi-style terrain with global-to-local control

    Zhang, J., Li, C., Zhou, P., Wang, C., He, G., and Qin, H., 2022. “Authoring multi-style terrain with global-to-local control”. Graphical Models, 119, p. 101122

    2022

  3. [11]

    Fractal terrain generation for vehicle simulation

    Dawkins, J. J., Bevly, D. M., and Jackson, R. L., 2012. “Fractal terrain generation for vehicle simulation”.International Journal of Vehicle Autonomous Systems,10(1), p. 3

    2012

  4. [12]

    Evaluation of fractal terrain model for vehicle dynamic simulations

    Dawkins, J. J., Bevly, D. M., and Jackson, R. L., 2012. “Evaluation of fractal terrain model for vehicle dynamic simulations”. Journal of Terramechanics,49(6), pp. 299–307

    2012

  5. [13]

    Model based off-road terrain profile estimation

    Dawkins, J. J., 2014. “Model based off-road terrain profile estimation”. In American Control Conference, IEEE, pp. 2792–2797

    2014

  6. [14]

    A multivariate weierstrass–mandelbrot function

    Ausloos, M., and Berman, D., 1985. “A multivariate weierstrass–mandelbrot function”. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1819), pp. 331–350

    1985

  7. [15]

    C., Carlucho, I., Albrecht, S

    Vecchio, G., Palazzo, S., Guastella, D. C., Carlucho, I., Albrecht, S. V ., Muscato, G., and Spampinato, C.,

  8. [16]

    Mississippi state university autonomous vehicle simulation library

    Hudson, C., Goodin, C., Miller, Z., Wheeler, W., and Carruth, D., 2020. “Mississippi state university autonomous vehicle simulation library”. In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium, pp. 11–13

    2020

  9. [17]

    A ros-based simulator for testing the enhanced autonomous navigation of the mars 2020 rover

    Toupet, O., Del Sesto, T., Ono, M., Myint, S., Vander Hook, J., and McHenry, M., 2020. “A ros-based simulator for testing the enhanced autonomous navigation of the mars 2020 rover”. In IEEE Aerospace Conference, IEEE, pp. 1–11

    2020

  10. [18]

    A review of physics simulators for robotic applications

    Collins, J., Chand, S., Vanderkop, A., and Howard, D., 2021. “A review of physics simulators for robotic applications”. IEEE Access, 9, pp. 51416–51431

    2021

  11. [19]

    Design and use paradigms for gazebo, an open-source multi-robot simulator

    Koenig, N., and Howard, A., 2004. “Design and use paradigms for gazebo, an open-source multi-robot simulator”. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), V ol. 3, IEEE, pp. 2149–2154

    2004

  12. [20]

    V-rep: A versatile and scalable robot simulation framework

    Rohmer, E., Singh, S. P. N., and Freese, M., 2013. “V-rep: A versatile and scalable robot simulation framework”. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1321–1326

    2013

  13. [21]

    Cyberbotics ltd. webots™: Professional mobile robot simulation

    Michel, O., 2004. “Cyberbotics ltd. webots™: Professional mobile robot simulation”. International Journal of Ad- vanced Robotic Systems, 1(1), p. 5

    2004

  14. [22]

    Unreal engine

    Epic Games, 2023. Unreal engine. Accessed January 5, 2023, from https://www.unrealengine.com

    2023

  15. [23]

    Unreal as a simulation environment for off-road autonomy

    Young, P., Kysar, S., and Bos, J. P., 2020. “Unreal as a simulation environment for off-road autonomy”. In Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure, V ol. 11415, International Society for Optics and Photonics, p. 114150F

    2020

  16. [24]

    Robot operating system

    ROS, 2023. Robot operating system. Accessed June 30, 2023, from https://www.ros.org

    2023

  17. [25]

    Clearpath Robotics, 2023. Husky. Accessed April 20, 2023, from https://clearpathrobotics.com/ husky-unmanned-ground-vehicle-robot/

    2023

  18. [26]

    NVIDIA, 2023. Physx. Accessed April 20, 2023, from https://developer.nvidia.com/physx-sdk

    2023

  19. [27]

    Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx

    Erez, T., Tassa, Y ., and Todorov, E., 2015. “Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx”. In IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 4397– 4404

    2015

  20. [28]

    Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments

    Kr ¨usi, P., Furgale, P., Bosse, M., and Siegwart, R., 2017. “Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments”. Journal of Field Robotics, 34(5), pp. 940– 984

    2017

  21. [29]

    Unstructured with a point: Validation and robustness evaluation of point-cloud based path planning

    Kysar, S., Bos, J., Kurup, A., Jeffries, Z., Carter, J., Majhor, C., Jayakumar, P., and Smith, W., 2021. Unstructured with a point: Validation and robustness evaluation of point-cloud based path planning. Tech. rep., SAE Technical Paper

    2021

  22. [30]

    Creating and using custom heightmaps and layers

    Epic Games, 2023. Creating and using custom heightmaps and layers. Accessed May 14, 2023, from https: //docs.unrealengine.com/4.27/en-US/BuildingWorlds/Landscape/Custom/

    2023

  23. [31]

    Robust edge detection based on modified moore-neighbor

    Biswas, S., and Hazra, R., 2018. “Robust edge detection based on modified moore-neighbor”. Optik, 168, pp. 931–943

    2018

  24. [32]

    gradient8

    Wolfgang Schwanghart, 2023. gradient8. Accessed January 30, 2023, from https://www.mathworks.com/ matlabcentral/fileexchange/18655-gradient8

    2023

  25. [2022]

    Midgard: A simulation platform for autonomous navigation in unstructured environments

    “Midgard: A simulation platform for autonomous navigation in unstructured environments”. arXiv preprint arXiv:2205.08389

    arXiv

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