3.5k citations · 10.5k across the 119 of their papers we have counts for
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Predicting Topological Maps for Visual Navigation in Unexplored Environments
Huangying Zhan, Hamid Rezatofighi, Ian Reid
We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be famil…
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Sourav Garg, Niko Sünderhauf, Feras Dayoub +9
For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…
Practical Robot Learning from Demonstrations using Deep End-to-End Training
Akansel Cosgun, Thomas Rowntree, Ian Reid +1
Robots need to learn behaviors in intuitive and practical ways for widespread deployment in human environments. To learn a robot behavior end-to-end, we train a variant of the ResN…
Real-Time Monocular Object-Model Aware Sparse SLAM
Mehdi Hosseinzadeh, Kejie Li, Yasir Latif +1
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While sparse point-based SLAM methods provide accurate camera localization, the generated…
Structure Aware SLAM using Quadrics and Planes
Mehdi Hosseinzadeh, Yasir Latif, Trung Pham +2
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps la…
Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place
M. McTaggart, D. Morrison, A. W. Tow +8
Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
Yasir Latif, Ravi Garg, Michael Milford +1
Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception ta…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…
A Discrete-Time Attitude Observer on SO(3) for Vision and GPS Fusion
Alireza Khosravian, Tat-Jun Chin, Ian Reid +1
This paper proposes a discrete-time geometric attitude observer for fusing monocular vision with GPS velocity measurements. The observer takes the relative transformations obtained…
A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration
Alireza Khosravian, Tat-Jun Chin, Ian Reid
We address the problem of camera-to-laser-scanner calibration using a checkerboard and multiple image-laser scan pairs. Distinguishing which laser points measure the checkerboard a…
Meaningful Maps With Object-Oriented Semantic Mapping
Niko Sünderhauf, Trung T. Pham, Yasir Latif +2
For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The maj…
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Cesar Cadena, Luca Carlone, Henry Carrillo +5
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving wit…