ROBOT-CENTRIC ELEVATION MAPPING WITH UNCERTAINTY ESTIMATES
Open accessAbstract
This paper addresses the local terrain mapping process for an autonomous robot. Building upon an onboard range measurement sensor and an existing robot pose estimation, we formulate a novel elevation mapping method from a robot-centric perspective. This formulation can explicitly handle drift of the robot pose estimation which occurs for many autonomous robots. Our mapping approach fully incorporates the distance sensor measurement uncertainties and the six-dimensional pose covariance of the robot. We introduce a computationally efficient formulation of the map fusion process, which allows for mapping a terrain at high update rates. Finally, our approach is demonstrated on a quadrupedal robot walking over obstacles.
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Existing terrain estimation pipelines that use cameras or LiDAR fail in environments with snow, water, or dense vegetation.
“Existing terrain estimation pipelines that use cameras or LiDAR [27] fail in environments with snow (Fig. 2A), water (Fig. 2C), or dense vegetation (Fig. 2F).”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [27]
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Learning quadrupedal locomotion over challenging terrain
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2020) · Science Robotics · doi:10.1126/scirobotics.abc5986 · Cited by 1,156 · see citing works
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- Authors
- FANKHAUSER, P., BLOESCH, M., GEHRING, C., HUTTER, M., & SIEGWART, R.
- Affiliations
- ETH Zurich (CH)
- Venue
- Mobile Service Robotics
- Publisher
- WORLD SCIENTIFIC
- Published
- 2014-07-24
- Pages
- 433-440
- OpenAlex
- W2139710904
- Cited by
- 192
- References
- 0
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 23 · 2023: 32 · 2022: 30 · 2021: 13 · 2020: 14 · 2019: 19 · 2018: 13 · 2017: 5 · 2016: 12 · 2015: 3
- FWCI
- 11.22
- Type
- conference paper
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- elevation mapping, topographic mapping, autonomous robots, map fusion, quadrupedal robots, obstacle navigation
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