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Related Concept Videos

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Kinetic Friction01:26

Kinetic Friction

Consider a truck trying to pull a stationary car. As the truck exerts a force on the car, static friction is created at the point of contact between the two surfaces. This frictional force resists the car's movement and keeps it at rest. However, when the applied force by the truck surpasses the limiting static frictional force, an interesting phenomenon occurs. The frictional force at the interface reduces to a lower value, known as the kinetic frictional force. At this point, the car begins...
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...
Slump Test01:20

Slump Test

The slump test is a widely used method to measure the workability of concrete. It employs a 12-inch high truncated cone mold that tapers from eight inches at the base to four inches at the top. Before testing, the mold is securely attached to a flat base and dampened.
Concrete is poured into the mold in three layers to conduct the test. Each layer is compacted 25 times with a steel tamping rod, which has a five-eighths-inch diameter and a rounded end, to ensure even distribution and eliminate...
Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Updated: Jun 13, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
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SY-SLAM: Real-Time Dynamic Indoor RGB-D SLAM with SuperPoint Detection and Asynchronous YOLOv8s-Based Keypoint

Shaoshuai Zhi1, Shuangfeng Wei1,2,3, Shan Zhou1

  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

SY-SLAM enhances visual Simultaneous Localization and Mapping (SLAM) for dynamic indoor environments by integrating SuperPoint and YOLO. This system significantly improves trajectory accuracy and stability in human-populated spaces.

Keywords:
RGB-D camerabounding-box-based keypoint suppressiondynamic environmentslearned keypoint detectorvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional visual SLAM struggles in dynamic indoor environments due to human motion.
  • Existing methods often degrade in accuracy and stability with frequent dynamic elements.

Purpose of the Study:

  • To develop a robust RGB-D SLAM system (SY-SLAM) for dynamic indoor environments.
  • To improve trajectory accuracy and front-end stability while maintaining real-time performance.

Main Methods:

  • Integrated a TensorRT-deployed, detector-only SuperPoint module for robust keypoint detection.
  • Implemented an adaptive SuperPoint keypoint selection strategy to balance quality and quantity.
  • Utilized an asynchronous YOLOv8s TensorRT thread for person detection and keypoint suppression.
  • Retained ORB binary descriptors within the ORB-SLAM3 framework for efficient matching.

Main Results:

  • SY-SLAM reduced Absolute Trajectory Error (ATE) Root Mean Square Error (RMSE) by 93.45% compared to ORB-SLAM3 on dynamic sequences.
  • Achieved competitive accuracy with state-of-the-art dynamic SLAM methods on the fr3/w/x sequence.
  • Maintained real-time performance at 46.8 Hz on a laptop GPU.

Conclusions:

  • SY-SLAM effectively handles dynamic indoor environments with frequent human motion.
  • The system demonstrates superior trajectory accuracy and front-end stability.
  • SY-SLAM offers a real-time, robust solution for visual SLAM in challenging dynamic scenarios.