EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy-Reference-Cited by-同舟云学术

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EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

Published:2024 Issue: Volume:40 Page:3756-3777
ISSN:1552-3098
Container-title:IEEE Transactions on Robotics
language:
Short-container-title:IEEE Trans. Robot.

Author:

Cai Xiaoyi¹^ORCID,Ancha Siddharth¹^ORCID,Sharma Lakshay¹^ORCID,Osteen Philip R.²^ORCID,Bucher Bernadette³^ORCID,Phillips Stephen⁴^ORCID,Wang Jiuguang⁴^ORCID,Everett Michael⁵^ORCID,Roy Nicholas¹^ORCID,How Jonathan P.¹^ORCID

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. DEVCOM Army Research Laboratory, Adelphi, MD, USA

3. University of Michigan, Ann Arbor, MI, USA

4. Boston Dynamics AI Institute, Cambridge, MA, USA

5. Northeastern University, Boston, MA, USA

Funder

ARL

ONR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8860/10365208/10606099.pdf?arnumber=10606099

Reference59 articles.

1. STEP: Stochastic traversability evaluation and planning for risk-aware off-road navigation;Fan,2021

2. ForestTrav: Accurate, efficient and deployable forest traversability estimation for autonomous ground vehicles;Ruetz,2023

3. TerrainNet: Visual modeling of complex terrain for high-speed, off-road navigation;Meng,2023

4. Semantic terrain classification for off-road autonomous driving;Shaban,2022

5. CALI: Coarse-to-fine alignments based unsupervised domain adaptation of traversability prediction for deployable autonomous navigation;Chen,2022

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