Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909177185566720 |
|---|---|
| author | Chung, Chanyoung Georgakis, Georgios Spieler, Patrick Padgett, Curtis Agha, Ali Khattak, Shehryar |
| author_facet | Chung, Chanyoung Georgakis, Georgios Spieler, Patrick Padgett, Curtis Agha, Ali Khattak, Shehryar |
| contents | Understanding terrain topology at long-range is crucial for the success of off-road robotic missions, especially when navigating at high-speeds. LiDAR sensors, which are currently heavily relied upon for geometric mapping, provide sparse measurements when mapping at greater distances. To address this challenge, we present a novel learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. Our proposed method is comprised of three main elements. First, a transformer-based encoder is introduced that learns cross-view associations between the egocentric views and prior bird-eye-view elevation map predictions. Second, an orientation-aware positional encoding is proposed to incorporate the 3D vehicle pose information over complex unstructured terrain with multi-view visual image features. Lastly, a history-augmented learn-able map embedding is proposed to achieve better temporal consistency between elevation map predictions to facilitate the downstream navigational tasks. We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain using real-world offroad driving data. Furthermore, the method is qualitatively and quantitatively compared against the current state-of-the-art methods. Extensive field experiments demonstrate that our method surpasses baseline models in accurately predicting terrain elevation while effectively capturing the overall terrain topology at long-ranges. Finally, ablation studies are conducted to highlight and understand the effect of key components of the proposed approach and validate their suitability to improve offroad robotic navigation capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17484 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation Chung, Chanyoung Georgakis, Georgios Spieler, Patrick Padgett, Curtis Agha, Ali Khattak, Shehryar Robotics Computer Vision and Pattern Recognition Machine Learning Understanding terrain topology at long-range is crucial for the success of off-road robotic missions, especially when navigating at high-speeds. LiDAR sensors, which are currently heavily relied upon for geometric mapping, provide sparse measurements when mapping at greater distances. To address this challenge, we present a novel learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. Our proposed method is comprised of three main elements. First, a transformer-based encoder is introduced that learns cross-view associations between the egocentric views and prior bird-eye-view elevation map predictions. Second, an orientation-aware positional encoding is proposed to incorporate the 3D vehicle pose information over complex unstructured terrain with multi-view visual image features. Lastly, a history-augmented learn-able map embedding is proposed to achieve better temporal consistency between elevation map predictions to facilitate the downstream navigational tasks. We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain using real-world offroad driving data. Furthermore, the method is qualitatively and quantitatively compared against the current state-of-the-art methods. Extensive field experiments demonstrate that our method surpasses baseline models in accurately predicting terrain elevation while effectively capturing the overall terrain topology at long-ranges. Finally, ablation studies are conducted to highlight and understand the effect of key components of the proposed approach and validate their suitability to improve offroad robotic navigation capabilities. |
| title | Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.17484 |