Robots depend on maps to move around, but they can’t use the regular GPS because it isn’t always accurate enough.
The reason why robots can’t depend on GPS is that it lacks accuracy and precision, which means that the robot could end up crashing into things inside your home causing you to replace parts or pay for costly damage repairs.
But don’t worry because there exists an alternative form of mapping for this kind of technology called Simultaneous Localization and Mapping, or SLAM.
SLAM works like a regular map but has the ability to understand where objects exist within a space and how best to handle them in order to prevent misalignment with the final objective.
Let’s find out more about this extraordinary technological advancement
What is LiDAR and SLAM
A LiDAR-based SLAM system uses a laser sensor to generate a 3D map of its environment. LiDAR measures the distance to an object by illuminating the object using an active laser pulse with an infrared beam.
Difference between localization and mapping
Mapping is the problem of collecting and correlating a multitude of data to create maps that can be used by drivers to navigate different routes. Making these maps more detailed is obviously advantageous but difficult given the need for accuracy, visual appeal, and overall creative freedom.
Localization is the problem of using data on maps to estimate your position with respect to this massive map – it’s critical on first time usage but overall creates a dependency on particular types of maps.
Localization must also deal with problems arising from sensor noise interference as well as uncertainty in map information that could result either from errors or creative freedom of design by various innovative artists
- A computer process used in robotics and augmented reality, SLAM is a procedure by which a computer can scan an area and create a virtual map of the territory.
- This process is often the backbone for augmented reality content that works to enhance real life environments.
- With ARKit apps, this procedure allows computers to build 3-D maps by tracking how mobile devices interact with surfaces in a physical space. Learn more about it here:Higher Education Teaching and Learning With Augmented RealitY
- SLAM is a way of localizing yourself within your surroundings while building up maps from sensor data you collect from them.
SLAM is used for Localization and Mapping
SLAM is a standard algorithm used in mapping and navigation. To do this, sensors on modern robots like quadrotors, microrobots and the like use SLAM to help them move around and through spaces more easily and with less chance of crashing into objects
Their highly sensitive laser rangefinders generate millions of data points per second which can be used to build up richer maps as they travel around an area.
SLAM algorithms then help align the sensor data taken at different sampling moments over time into a single map (using some clever math such as bundle adjustment).
Moreover, these algorithms can be accelerated on GPGPUs thanks to their great parallel processing capabilities!
Sensor Data Alignment
Today’s computers consider the position of a robot as a timestamp dot on a timeline or a map. Besides, robots continue to collect data about their surroundings using these sensors.
The interesting part is that camera images are captured 90 times per second for proper measurements. When robots move around, data points make it easier for the robot to prevent accidents.
Thus, their performance will be much better when they can take advantage of high-tech tools designed to make them work in different situations.
Robot odometry considers the rotation of the wheels of the robot.
It helps the robot count how far it has moved. Inertial Measurement Units (IMUs) allow robots to measure their acceleration and speed as well as how much distance they have travelled, based on wheel rotation.
Sensor Data Registration
Using a map as a boundary, the first step before registering data between two measurements is scanning to produce a point cloud. Using this point cloud as a reference, developers can easily locate an obstacle using scan-to-map matching.
GPUs that perform Split-Second Calculations
These mapping measurements are calculated between 20 and 100 times each second. The speed depends upon the algorithms, and while that might be inconvenient, it works to your advantage.
Unlike a regular CPU (central processing unit) that can take five minutes to calculate a mapping, this new system uses powerful graphics processing units (GPUs), which can make similar calculations in seconds or less! This makes S.L.A.M. more accurate than regular mapping technology, meaning you won’t spend time wandering around in circles like you do with GPS (global positioning systems) on some of the old phones (ahem…Motorolas…).
Take advantage of the speedy processors in today’s most powerful phones with SLAM technology—make sure it’s an up-to-date phone with a smartphone operating system for seamless execution every single time!
Visual Odometry to help with Localization
The goal of visual odometry is to allow a robot to properly navigate where it’s located, and to achieve this on its own, so it doesn’t need any kind of external guidance.
Currently, robotic developers use two cameras in tandem that allows them to calculate the exact position and orientation of a robot at any given time, and what’s more: they do this in real-time at a speed of 30 frames per second.
Map Building that helps with Localization
The process of creating a map simultaneously is known as Simultaneous Localization and Mapping or better known as SLAM. There are three different methods of how to work this process.
In the first method, the SLAM algorithm working under supervision of a supervisor. This means that the whole job is controlled manually.
On the other hand, in the second method, there is no supervisor involved. The whole job is done by the power of a computer.
What is localization and mapping in robotics
SLAM (Simultaneous Localization And Mapping) is the computational problem in robotics navigation and mapping where it constructs and updates the map of an unknown environment, and simultaneously locates the robot’s position within it (Durrant-Whyte and Bailey, 2006).