scholarly journals A probabilistic framework for next best view estimation in a cluttered environment

2014 ◽  
Vol 25 (1) ◽  
pp. 148-164 ◽  
Author(s):  
Christian Potthast ◽  
Gaurav S. Sukhatme
Author(s):  
Linda Cundy

Working online during lockdown, psychotherapists have glimpsed inside their clients' homes and sometimes we see a cluttered environment. This article addresses the difficulty experienced by many people of parting with objects, proposing that we identify with our possessions and invest them with feelings in line with our own experiences.


Author(s):  
Mats Alvesson ◽  
Yiannis Gabriel ◽  
Roland Paulsen

This book argues that we are currently witnessing not merely a decline in the quality of social science research, but a proliferation of meaningless research of no value to society and modest value to its authors—apart from securing employment and promotion. The explosion of published outputs, at least in social science, creates a noisy, cluttered environment which makes meaningful research difficult, as different voices compete to capture the limelight even briefly. Older, but more impressive contributions are easily neglected as the premium is to write and publish, not read and learn. The result is a widespread cynicism among academics on the value of academic research, sometimes including their own. Publishing comes to be seen as a game of hits and misses, devoid of intrinsic meaning and value and of no wider social uses whatsoever. This is what the book views as the rise of nonsense in academic research, which represents a serious social problem. It undermines the very point of social science. This problem is far from ‘academic’. It affects many areas of social and political life entailing extensive waste of resources and inflated student fees as well as costs to taxpayers. The book’s second part offers a range of proposals aimed at restoring meaning at the heart of social science research, and drawing social science back, address the major problems and issues that face our societies.


Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Maxime Vaidis ◽  
Martin J.-D. Otis

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages.


2010 ◽  
Vol 26 (16) ◽  
pp. 1950-1957 ◽  
Author(s):  
Yin Hu ◽  
Kai Wang ◽  
Xiaping He ◽  
Derek Y. Chiang ◽  
Jan F. Prins ◽  
...  

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 287
Author(s):  
Matteo Corno ◽  
Sara Furioli ◽  
Paolo Cesana ◽  
Sergio M. Savaresi

Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees.


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