Wednesday, June 11, 2025

Simultaneous Localization Mapping (SLAM) #Sciencefather #researchawards #computervision

 

 Introduction

The technology industry is inundated with references to AI (artificial intelligence), ML (machine learning), DNN’s (deep neural networks), CV (computer vision), CNN’s (convolutional neural networks), RNN’s (recurrent neural networks), etc..  

What these acronyms represent are some of the components that make up the field of Artificial Intelligence. Imagine an artificial being, and what it needs to successfully interact with the world around it - the ability to sense and perceive its environment (machine perception), the ability to understand speech (natural language processing), the ability to remember information, learn new things, and make inferences (machine learning, knowledge management and reasoning), the ability to plan and execute actions (automated planning), and the ability to interact with its environment (robotics).

Machine perception encompasses the capabilities enabling machines to understand the input from the 5 senses - visual, auditory, tactile, olfactory, and gustatory. (Yes, they do have machines that analyze smell and taste).

Computer Vision and SLAM

Buried among these acronyms, you may have come across references to computer vision and SLAM. Let’s dive into the arena of computer vision and where SLAM fits in. There are a number of different flavors of SLAM, such as topological, semantic and various hybrid approaches, but we’ll start with an illustration of metric SLAM.

As the name suggests (intuitively or not), Simultaneous Localization and Mapping is the capability for a machine agent to sense and create (and constantly update) a representation of its surrounding environment (this is the mapping part), and understand its position and orientation within that environment (this is the localization part). Most humans do this well enough without much effort, but trying to get a computer to do this is another matter.

There are many types of sensors that can detect the surrounding environment, including camera(s), LiDAR, radar, and sonar. As the machine agent with the sensors (such as the ones listed prior) moves through space, a snapshot of the environment is created, while the relative position of the machine agent within that space is tracked. Thus, a picture is formed by features represented as points in space including the relative distance from the observer and with each other.

Over time, this collection of feature points and their registered position in space grow together to form a point cloud, a 3-dimensional representation of the environment. This is the “mapping” part in SLAM.

As the map is being created, the machine agent tracks its relative position and orientation within that point cloud, enabling the “localization” part in SLAM. Once a map is available, then any arbitrary machine agent using the map would be able to “relocalize” within that space - ie. determine its location on the map from what it perceives around it.


International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.


Visit Our Website : computer.scifat.com 

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