Pose invariant people detection in point clouds for mobile robots

2020-05-01
To be able to navigate in socially complaint fashion and safely, people detection is a very important ability for robots deployed in our social environments. However, it is a challenging task since humans exhibit various poses in daily life as they bend, sit down, touch or interact with each other. A robust people detector should detect humans also in these arbitrary poses. In addition, mobile robots should be able to carry out detection in a real-time manner because our environment is highly dynamic. In this study we developed a fast head and people detector which can, pose invariantly, detect people. Method depends only on depth information of point clouds taken from RGB-D sensors. As a result, it is robust against sudden light and contrast changes. The algorithm runs relying only on CPU, which makes it applicable to mobile robots with low computational resources.
International Journal of Mechanical Engineering and Robotics Research

Suggestions

Pose Invariant People Detection in Point Clouds for Mobile Robots
Hacınecipoğlu, Akif; Konukseven, Erhan İlhan; Koku, Ahmet Buğra (2019-10-28)
To be able to navigate in socially complaint fashion and safely, people detection is a very important ability for robots deployed in our social environments. However, it is a challenging task since humans exhibit various poses in daily life as they bend, sit down, touch or interact with each other. A robust people detector should detect humans also in these arbitrary poses. In addition, mobile robots should be able to carry out detection in a real-time manner because our environment is highly dynamic. In th...
Human aware navigation of a mobile robot in crowded dynamic environments
Hacınecipoğlu, Akif; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2019)
As mobile robots start operating in dynamic environments crowded with humans, human-aware and human-like navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People can navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear path leading to it. This is clearly a dexterity of humans. But the mobile robots which have to navigate in such environments lack this feature. Even perf...
Multiple human trajectory prediction and cooperative navigation modeling in crowded scenes
Hacinecipoglu, Akif; Konukseven, Erhan İlhan; Koku, Ahmet Buğra (Springer Science and Business Media LLC, 2020-07-01)
As mobile robots start operating in environments crowded with humans, human-aware navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear route leading to it. There are significant efforts to solve this problem for mobile robots; however, they are not scalable to high human density and learning based approaches depen...
GRU-GBM: A combined intrusion detection model using LightGBM and gated recurrent unit
Sarikaya, Alper; Günel Kılıç, Banu; DEMİRCİ, MEHMET (2022-07-01)
Due to the increasing sophistication of cyber-attacks, intrusion detection systems need to be improved constantly. Each machine learning classifier has different advantages against intrusion detection and combining the advantages of different classifiers increases detection rates. In this study, we combine a machine learning classifier with a deep learning model to propose a new approach called GRU-GBM. The LightGBM gradient boosting machine framework is used for feature selection, and each feature in the d...
GENERATION AND ANALYSIS OF "BREATHING" AS AN HRI BEHAVIOR ON A COBOT
Güneşdoğdu, Ali Nuri; Şahin, Erol; Acartürk, Cengiz; Department of Computer Engineering (2022-8-31)
Collaborative robots, a.k.a cobots, are industrial robotic manipulators that have no built-in capabilities for social human-robot interaction (HRI). In the thesis, we implemented breathing for a cobot as a social behavior inspired by the secondary action animation principle. We automatically generated breathing of a cobot as HRI behavior with its waveform borrowed from human breathing; its amplitude and frequency are parametrized. We conducted a user study to measure the effect of parameters of breathing b...
Citation Formats
A. Hacinecipoglu, E. İ. Konukseven, and A. B. Koku, “Pose invariant people detection in point clouds for mobile robots,” International Journal of Mechanical Engineering and Robotics Research, pp. 709–715, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38890.