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Related Concept Videos

Autonomic Nervous System01:22

Autonomic Nervous System

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The autonomic nervous system (ANS) is a critical component of the peripheral nervous system, primarily responsible for regulating involuntary bodily functions and maintaining homeostasis. It functions in tandem with the central nervous system (CNS) to seamlessly coordinate various physiological processes without the need for conscious control.
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Autonomic Nervous System: Overview01:26

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The human nervous system is divided into two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS is composed of the brain and spinal cord, while the PNS contains nerve cells, clusters of nerve cells, and the sensory receptors that are outside the CNS. The PNS has two types of nerve cells: sensory (afferent) and motor (efferent). Sensory cells send signals to the CNS from receptors, and motor cells carry signals from the CNS to organs, muscles, and...
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The autonomic nervous system (ANS) is an intricate network of nerves that controls functions such as the regulation of heart rate, digestion, and blood pressure regulation. When this system malfunctions, it can lead to various disorders that affect multiple bodily functions. One common feature of many autonomic disorders is the involvement of smooth blood vessels, which play a crucial role in regulating blood flow throughout the body.
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Ganglionic stimulants activate NM nicotinic receptors in autonomic ganglia, falling into two categories: nicotine mimetics [e.g., lobeline, dimethylpiperazine, tetramethylammonium] and muscarinic receptor agonists [e.g., muscarine, methacholine]. The first category's action is rapid and blocked by nicotinic receptor antagonists, while the second category's action is delayed and blocked by atropine-like agents. Nicotine, an alkaloid, affects the heart rate by stimulating...
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Drugs Acting on Autonomic Ganglia: Blockers01:28

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Ganglionic blockers inhibit autonomic activity by blocking nicotinic receptors in the autonomic ganglia, suppressing impulse transmission. These blockers lack selectivity between sympathetic and parasympathetic ganglia and are ineffective as neuromuscular junction antagonists. They can be categorized into two groups:
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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Updated: Feb 6, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Lightweight Visual Odometry for Autonomous Mobile Robots.

Mohamed Aladem1, Samir A Rawashdeh2

  • 1College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA. maladem@umich.edu.

Sensors (Basel, Switzerland)
|August 30, 2018
PubMed
Summary

This study introduces a real-time visual odometry system for mobile robot localization. The system achieves high accuracy with lower memory and computational costs than traditional methods.

Keywords:
RGB-Dego-motion estimationmobile robotsstereovisual odometry

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Area of Science:

  • Robotics
  • Computer Vision

Background:

  • Mobile robot localization is crucial for navigation and path planning.
  • Vision-based motion estimation, or visual odometry, is a key technology in this field.
  • Existing methods often require significant computational resources or lack accuracy.

Purpose of the Study:

  • To present a low-overhead, real-time ego-motion estimation system using stereo or RGB-D sensors.
  • To improve accuracy over frame-to-frame approaches while reducing resource demands.
  • To provide a publicly available, modular software package for the robotics community.

Main Methods:

  • Developed a visual odometry algorithm utilizing a limited local map.
  • Employed stereo or RGB-D sensors for motion estimation.
  • Integrated the system with the Robot Operating System (ROS).

Main Results:

  • The proposed algorithm demonstrated superior accuracy compared to typical frame-to-frame methods.
  • The system requires significantly less memory and computational power than global map-based SLAM.
  • Performance was validated on public datasets and compared against open-source alternatives.

Conclusions:

  • The developed visual odometry system offers an efficient and accurate solution for mobile robot localization.
  • The low-overhead approach makes it suitable for real-time applications with limited resources.
  • The open-source release facilitates adoption and further development within the robotics community.