Simultaneous localization and mapping patent
                     Simultaneous 
localization and 
mapping patent
Exploring Advances in Simultaneous Localization and Mapping
Introduction
This presentation explores 
recent breakthroughs in 
SLAM technology, 
emphasizing a 
groundbreaking algorithm 
that boosts efficiency in 
ever-changing environments. 
We will discuss what SLAM 
is, its fundamental elements, 
and how these 
advancements can transform 
robotics and augmented 
reality fields.
01
SLAM Overview
Definition of 
SLAM
Simultaneous Localization and 
Mapping (SLAM) is a process 
that allows a robot or device to 
map an unknown environment 
while keeping track of its 
location within that 
environment. This dual task is 
crucial for autonomous 
navigation in robotics and AR 
systems.
Key components 
of SLAM 
technology
SLAM consists of several key 
components including sensors for 
environmental data collection, 
algorithms for processing this data, 
and motion models to predict 
movement. These components 
work in unison to create accurate 
maps while continually updating 
the device's position.
Applications in 
robotics and AR
SLAM technology is pivotal in various 
fields including robotics for autonomous 
navigation, where robots must adapt to 
new environments. In augmented 
reality, SLAM enables overlaying virtual 
content on the real world, enhancing 
user experience in applications like 
gaming, training simulations, and 
industrial applications.
02
Innovative Algorithm
Enhancement
s in accuracy
The novel algorithm improves 
SLAM's accuracy by utilizing 
advanced techniques like adaptive 
filtering and machine learning to 
process sensor data more 
effectively. This results in better 
spatial awareness and mapping 
quality, especially in challenging 
environments with dynamic 
obstacles.
Improved 
speed in 
dynamic 
environments
With the introduction of real-time 
processing capabilities, the 
algorithm allows for quicker 
responses to changing conditions. 
This means that a robot can adapt 
its route or the map it is creating 
without delays, making it more 
efficient in environments where 
obstacles might appear suddenly.
Integration of sensor data 
and predictive modeling
The algorithm merges various sensor 
inputs—such as LIDAR, cameras, and 
inertial measurement units—with 
predictive modeling techniques. This 
integration allows for enhanced 
situational awareness and 
contributes to more accurate 
mapping and localization in real-
time.
Conclusions
The advancements in SLAM 
technology, particularly through 
innovative algorithms, 
significantly enhance both the 
accuracy and speed of 
navigation in robotics and 
augmented reality. These 
improvements hold the potential 
to transform how machines 
interact with their environments 
and augment user experiences.
Thank you!
Do you have any questions?
Read more here:
https://patents.justia.com/patent/12
092467