scholarly journals STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior

2019 ◽  
Vol 13 ◽  
Author(s):  
Shuhei J. Yamazaki ◽  
Kazuya Ohara ◽  
Kentaro Ito ◽  
Nobuo Kokubun ◽  
Takuma Kitanishi ◽  
...  
Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4036 ◽  
Author(s):  
Chaofan Zhang ◽  
Yong Liu ◽  
Fan Wang ◽  
Yingwei Xia ◽  
Wen Zhang

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it’s the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono.


Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.


1998 ◽  
Vol 3 (1) ◽  
pp. 13-36 ◽  
Author(s):  
Ruth Guttman ◽  
Charles W. Greenbaum

This article gives an overview of Facet Theory, a systematic approach to facilitating theory construction, research design, and data analysis for complex studies, that is particularly appropriate to the behavioral and social sciences. Facet Theory is based on (1) a definitional framework for a universe of observations in the area of study; (2) empirical structures of observations within this framework; (3) a search for correspondence between the definitional system and aspects of the empirical structure for the observations. The development of Facet Theory and Facet Design is reviewed from early scale analysis and the Guttman Scale, leading to the concepts of “mapping sentence,” “universe of content,” “common range,” “content facets,” and nonmetric multidimensional methods of data analysis. In Facet Theory, the definition of the behavioral domain provides a rationale for hypothesizing structural relationships among variables employed in a study. Examples are presented from various areas of research (intelligence, infant development, animal behavior, etc.) to illustrate the methods and results of structural analysis with Smallest Space Analysis (SSA), Multidimensional Scalogram Analysis (MSA), and Partial Order Scalogram Analysis (POSA). The “radex” and “cylindrex” of intelligence tests are shown to be outstanding examples of predicted spatial configurations that have demonstrated the ubiquitous emergence of the same empirical structures in different studies. Further examples are given from studies of spatial abilities, infant development, animal behavior, and others. The use of Facet Theory, with careful construction of theory and design, is shown to provide new insights into existing data; it allows for the diagnosis and discrimination of behavioral traits and makes the generalizability and replication of findings possible, which in turn makes possible the discovery of lawfulness. Achievements, issues, and future challenges of Facet Theory are discussed.


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