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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Updated: Sep 3, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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LTC-Mapping, Enhancing Long-Term Consistency of Object-Oriented Semantic Maps in Robotics.

Jose-Luis Matez-Bandera1, David Fernandez-Chaves1,2, Jose-Raul Ruiz-Sarmiento1

  • 1Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29016 Malaga, Spain.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

LTC-Mapping creates consistent object-oriented semantic maps for mobile robots by preventing duplicate object instances and handling dynamic scenes. This method ensures accurate long-term robot operation in changing environments.

Keywords:
Detectron2dynamic scenesinstance duplicationlong-term consistencymobile robotsobject detectionobject-oriented mapssemantic maps

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Mobile robot operation relies on accurate environmental mapping.
  • Existing object-oriented mapping methods struggle with instance duplication and dynamic scenes.
  • Maintaining long-term map consistency is crucial for robot autonomy.

Purpose of the Study:

  • To propose LTC-Mapping, a novel method for building long-term consistent object-oriented semantic maps.
  • To address challenges of instance duplication and dynamic scenes in robot mapping.
  • To enhance the reliability and accuracy of semantic maps for mobile robots.

Main Methods:

  • Modeling detected objects using 3D bounding boxes and analyzing vertex visibility for occlusion detection.
  • Augmenting geometric models with semantic information for object categorization.
  • Employing data association and fusion techniques for temporal propagation of geometric and semantic data.
  • Implementing a mechanism to remove objects from the map based on non-detection evidence to handle scene dynamics.

Main Results:

  • LTC-Mapping demonstrates superior performance in modeling both geometric and semantic information of objects compared to state-of-the-art alternatives.
  • The method effectively prevents instance duplication by analyzing object visibility and occlusions.
  • LTC-Mapping successfully handles dynamic scenes, maintaining map accuracy over time.
  • Experimental validation using the Robot@VirtualHome ecosystem confirms the method's effectiveness and suitability for online execution.

Conclusions:

  • LTC-Mapping provides a robust solution for long-term consistent object-oriented semantic mapping in mobile robotics.
  • The proposed approach enhances robot perception in complex and dynamic environments.
  • LTC-Mapping offers a significant advancement for autonomous systems requiring accurate and persistent environmental understanding.