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2018 Conference Papers [12]

A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration

Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In Science & Engineering Faculty. Wellington, New Zealand: Springer. Retrieved from https://eprints.qut.edu.au/121640/

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Dropout Sampling for Robust Object Detection in Open-Set Conditions.

Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–7). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8460700

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SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

Pham, T. T., Do, T.-T., Sunderhauf, N., & Reid, I. (2018). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–9). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8461108

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Bootstrapping the Performance of Webly Supervised Semantic Segmentation

Shen, T., Lin, G., Shen, C., & Reid, I. (2018). Bootstrapping the Performance of Webly Supervised Semantic Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah, United States. Retrieved from http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1401.pdf

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Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

Weerasekera, C., Dharmasiri, T., Garg, R., Drummond, T., & Reid, I. (2017). Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors.

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SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

Pham, T., Do, T.-T., Sünderhauf, N., & Reid, I. (2017). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. Retrieved from http://arxiv.org/abs/1709.07158

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Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

Park, C., Moghadam, P., Kim, S., Elfes, A., Fookes, C., & Sridharan, S. (2017). Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM. Retrieved from http://arxiv.org/abs/1711.01691

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Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

Morrison, D., Tow, A. W., McTaggart, M., Smith, R., Kelly-Boxall, N., Wade-McCue, S., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Kumar, V., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2017). Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. Retrieved from https://arxiv.org/abs/1709.06283

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Dropout Sampling for Robust Object Detection in Open-Set Conditions

Miller, D., Nicholson, L., Dayoub, F., & Sünderhauf, N. (2017). Dropout Sampling for Robust Object Detection in Open-Set Conditions. Retrieved from http://arxiv.org/abs/1710.06677

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Semantic Segmentation from Limited Training Data

Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, N., Lee, D., McTaggart, M., Rallos, G., Razjigaev, A., Rowntree, T., Shen, T., Smith, R., Wade-McCue, S., Zhuang, Z., Lehnert, C., Lin, G., Reid, I., Corke, P., & Leitner, J. (2017). Semantic Segmentation from Limited Training Data. Retrieved from http://arxiv.org/abs/1709.07665

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Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

McCool, C. S., Beattie, J., Firn, J., Lehnert, C., Kulk, J., Bawden, O., Russell, R., & Perez, T. (2018). Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 1–1. http://doi.org/10.1109/LRA.2018.2794619

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Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

Latif, Y., Garg, R., Milford, M., & Reid, I. (2017). Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks. Retrieved from http://arxiv.org/abs/1709.08810

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