A Survey of Traversability Estimation for Mobile Robots
Traversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major st...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9869644/ |
| _version_ | 1798035822835924992 |
|---|---|
| author | Christos Sevastopoulos Stasinos Konstantopoulos |
| author_facet | Christos Sevastopoulos Stasinos Konstantopoulos |
| author_sort | Christos Sevastopoulos |
| collection | DOAJ |
| description | Traversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major steps in the evolution of traversability estimation techniques, covering both non-trainable and machine-learning methods, leading up to the recent proliferation of deep learning literature. We discuss how the nascence of Deep Learning has created an opportunity for radical improvement in traversability estimation. Finally, we discuss how self-supervised learning can help satisfy deep methods’ increased need for (challenging to acquire and label) large-scale datasets. |
| first_indexed | 2024-04-11T21:03:37Z |
| format | Article |
| id | doaj.art-1f3a41fb3ecc4443b6507447fa521877 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T21:03:37Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1f3a41fb3ecc4443b6507447fa5218772022-12-22T04:03:24ZengIEEEIEEE Access2169-35362022-01-0110963319634710.1109/ACCESS.2022.32025459869644A Survey of Traversability Estimation for Mobile RobotsChristos Sevastopoulos0https://orcid.org/0000-0001-8978-1402Stasinos Konstantopoulos1https://orcid.org/0000-0002-2586-1726Department of Computer Science and Computer Engineering, University of Texas at Arlington, Arlington, TX, USAInstitute of Informatics and Telecommunications, NCSR “Demokritos,”, Agia Paraskevi, GreeceTraversability illustrates the difficulty of driving through a specific region and encompasses the suitability of the terrain for traverse based on its physical properties, such as slope and roughness, surface condition, etc. In this survey we highlight the merits and limitations of all the major steps in the evolution of traversability estimation techniques, covering both non-trainable and machine-learning methods, leading up to the recent proliferation of deep learning literature. We discuss how the nascence of Deep Learning has created an opportunity for radical improvement in traversability estimation. Finally, we discuss how self-supervised learning can help satisfy deep methods’ increased need for (challenging to acquire and label) large-scale datasets.https://ieeexplore.ieee.org/document/9869644/Mobile robotstraversability estimationdeep learningrobot perceptionmachine learningdata-driven |
| spellingShingle | Christos Sevastopoulos Stasinos Konstantopoulos A Survey of Traversability Estimation for Mobile Robots IEEE Access Mobile robots traversability estimation deep learning robot perception machine learning data-driven |
| title | A Survey of Traversability Estimation for Mobile Robots |
| title_full | A Survey of Traversability Estimation for Mobile Robots |
| title_fullStr | A Survey of Traversability Estimation for Mobile Robots |
| title_full_unstemmed | A Survey of Traversability Estimation for Mobile Robots |
| title_short | A Survey of Traversability Estimation for Mobile Robots |
| title_sort | survey of traversability estimation for mobile robots |
| topic | Mobile robots traversability estimation deep learning robot perception machine learning data-driven |
| url | https://ieeexplore.ieee.org/document/9869644/ |
| work_keys_str_mv | AT christossevastopoulos asurveyoftraversabilityestimationformobilerobots AT stasinoskonstantopoulos asurveyoftraversabilityestimationformobilerobots AT christossevastopoulos surveyoftraversabilityestimationformobilerobots AT stasinoskonstantopoulos surveyoftraversabilityestimationformobilerobots |