dc.contributor.author | Kulkarni, Mihir | |
dc.contributor.author | Nguyen, Dinh Huan | |
dc.contributor.author | Alexis, Konstantinos | |
dc.date.accessioned | 2024-04-10T06:51:07Z | |
dc.date.available | 2024-04-10T06:51:07Z | |
dc.date.created | 2023-11-13T11:31:20Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2153-0858 | |
dc.identifier.uri | https://hdl.handle.net/11250/3125646 | |
dc.description.abstract | This paper contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both real-world and simulated depth images to compress the input data, while preserving semantically-labeled thin obstacles and handling invalid pixels in the depth sensor's output. This compressed representation, in addition to the robot's partial state involving its linear/angular velocities and its attitude are then utilized to train an uncertainty-aware 3D Collision Prediction Network in simulation to predict collision scores for candidate action sequences in a predefined motion primitives library. A set of simulation and experimental studies in cluttered environments with various sizes and types of obstacles, including multiple hard-to-perceive thin objects, were conducted to evaluate the performance of the proposed method and compare against an end-to-end trained baseline. The results demonstrate the benefits of the proposed semantically-enhanced deep collision prediction for learning-based autonomous navigation. | en_US |
dc.description.abstract | Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots | en_US |
dc.title.alternative | Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.journal | IEEE International Conference on Intelligent Robots and Systems. Proceedings | en_US |
dc.identifier.doi | 10.1109/IROS55552.2023.10342297 | |
dc.identifier.cristin | 2195661 | |
dc.relation.project | Andre: AFOSR: RESNAV. Award No. FA8655-21-1- 7033 | en_US |
dc.relation.project | Norges forskningsråd: 321435 | en_US |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |