TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation Article Swipe
Xiangyun Meng
,
Nathan O. Hatch
,
Alexander Lambert
,
Anqi Li
,
Nolan Wagener
,
Matthew Schmittle
,
Joon-Ho Lee
,
Wentao Yuan
,
Zoey Chen
,
Sameul Deng
,
Greg Okopal
,
Dieter Fox
,
Byron Boots
,
Amirreza Shaban
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.15607/rss.2023.xix.103
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.15607/rss.2023.xix.103
Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime.Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain.To this end, we present TerrainNet, a vision-based terrain perception system for semantic and geometric terrain prediction for aggressive, off-road navigation.The approach relies on several key insights and practical considerations for achieving reliable terrain modeling.The network includes a multi-headed output representation to capture fine-and coarse-grained terrain features necessary for estimating traversability.Accurate depth estimation is achieved using self-supervised depth completion with multi-view RGB and stereo inputs.Requirements for real-time performance and fast inference speeds are met using efficient, learned image feature projections.Furthermore, the model is trained on a largescale, real-world off-road dataset collected across a variety of diverse outdoor environments.We show how TerrainNet can also be used for costmap prediction and provide a detailed framework for integration into a planning module.We demonstrate the performance of TerrainNet through extensive comparison to current state-of-the-art baselines for camera-only scene prediction.Finally, we showcase the effectiveness of integrating TerrainNet within a complete autonomous-driving stack by conducting a real-world vehicle test in a challenging off-road scenario. Base Min Ground ElevationCeiling Elevation
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15607/rss.2023.xix.103
- https://doi.org/10.15607/rss.2023.xix.103
- OA Status
- gold
- Cited By
- 40
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385416313
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4385416313Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.15607/rss.2023.xix.103Digital Object Identifier
- Title
-
TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road NavigationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-10Full publication date if available
- Authors
-
Xiangyun Meng, Nathan O. Hatch, Alexander Lambert, Anqi Li, Nolan Wagener, Matthew Schmittle, Joon-Ho Lee, Wentao Yuan, Zoey Chen, Sameul Deng, Greg Okopal, Dieter Fox, Byron Boots, Amirreza ShabanList of authors in order
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https://doi.org/10.15607/rss.2023.xix.103Publisher landing page
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https://doi.org/10.15607/rss.2023.xix.103Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.15607/rss.2023.xix.103Direct OA link when available
- Concepts
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Terrain, Computer science, Computer vision, Computer graphics (images), Artificial intelligence, Geography, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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40Total citation count in OpenAlex
- Citations by year (recent)
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2025: 21, 2024: 19Per-year citation counts (last 5 years)
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69Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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