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As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. They do not only have to operate efficiently and safely in natural, populated environments, but also be able to achieve higher levels of cooperation and communication with humans. Human–robot collaboration (HRC) is a research field with a wide range of applications, future scenarios, and potentially a high economic impact. HRC is an interdisciplinary research area comprising classical robotics, cognitive sciences, and psychology. This paper gives a survey of the state of the art of HRC. Established methods for intention estimation, action planning, joint action, and machine learning are presented together with existing guidelines to hardware design. This paper is meant to provide the reader with a good overview of technologies and methods for HRC.

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In robotics applications such as SLAM (Simultaneous Localization and Mapping), loop closure detection is an integral component required to build a consistent topological or metric map. This paper presents an appearance based loop closure detection mechanism titled 'IBuILD' (Incremental bag of BInary words for Appearance based Loop closure Detection). The presented approach focuses on an online, incremental formulation of binary vocabulary generation for loop closure detection. The proposed approach does not require a prior vocabulary learning phase and relies purely on the appearance of the scene for loop closure detection without the need of odometry or GPS estimates. The vocabulary generation process is based on feature tracking between consecutive images to incorporate pose invariance. In addition, this process is coupled with a simple likelihood function to generate the most suitable loop closure candidate and a temporal consistency constraint to filter out inconsistent loop closures. Evaluation on different publicly available outdoor urban and indoor datasets shows that the presented approach is capable of generating higher recall at 100% precision in comparison to the state of the art.