Introduction to Simultaneous, Localization and Mapping (SLAM): All You Need to Know

Simultaneous, Localization and Mapping (SLAM) is a cutting-edge technology that allows robots to build maps as they move around, ensuring they always know their position and surroundings. 

 

It is a complex and multi-stage process that involves aligning sensor data using sophisticated algorithms perfectly suited for the parallel processing capabilities of GPUs. Despite being around since the 1980s, SLAM continues to advance and find new applications, making it an exciting and rapidly evolving field. 

SLAM (Simultaneous, Localization and Mapping): Meaning Explained

SLAM, which stands for Simultaneous, Localization, and Mapping, was coined by Hugh Durrant-Whyte and John Leonard in the early 1990s. Originally known as SMAL, the term was changed to SLAM to make a greater impact. The primary objective of SLAM is to develop an algorithm that can build a map of an unknown environment while simultaneously navigating through it.

The development of SLAM has been critical to the advancement of mobile robotics, which requires robots to perform tasks and navigate complex environments indoors and outdoors without human input. Based on the constructed maps, the robot needs to localize itself within its environment. The spatial information of the environment is mapped on-the-fly, with no prior knowledge of the robot's location. The robot then uses the built map for navigation.

There are many ways to implement SLAM, which is more of a concept than a single algorithm. Several different algorithms can be used to implement the various steps involved in SLAM. SLAM is highly dependent on visual data, sensor data, point clouds, and rapid processing, which must work simultaneously.

SLAM systems typically have two main components: 

• Range measurement and 
• Data extraction. 

The range measurement component involves using devices such as image sensors, cameras, LiDAR laser scanners, or sonar to observe and measure the physical properties of the environment around the robot or vehicle.

Once the sensory data is collected, the data extraction component comes into play. It involves using software that interprets the data to identify landmarks within the environment. This component can employ a variety of algorithms and scan-matching techniques.

For a SLAM system to work correctly, there needs to be a constant interplay between the range measurement device, data extraction software, the robot or vehicle, and any additional hardware or processing technologies. Each component can vary depending on the use case, but all must work seamlessly together to explore the environment accurately.

What Does Simultaneous, Localization and Mapping (SLAM) Software Do?

Simultaneous, Localization and Mapping (SLAM) software is used in robotics to create a map of an unknown environment while simultaneously localizing the robot. The process involves using sensors such as lidar, cameras, or sonar to gather information about the environment and the robot's movement.

The SLAM software uses this data to construct a map of the environment and simultaneously estimate the robot's position within the map. It is a challenging problem because the robot must accurately sense its surroundings and track its movements relative to the map.

The output of a SLAM system is typically a map of the environment and an estimate of the robot's current pose (position and orientatio.0n) within that map. This information can be used for various tasks such as navigation, object recognition, and obstacle avoidance.

How Does Simultaneous, Localization and Mapping (SLAM) Work?

To navigate an unknown environment, a vehicle or robot equipped with SLAM technology identifies various markers and signs within its surroundings, similar to how humans would do the same thing.

For example, when person is lost in an unfamiliar place, they might scan their surroundings and look for easily identifiable landmarks. Even if the person has never seen this location, they can still note the landmark.

Once the person has identified a landmark, they must determine the relative position to navigate effectively.

SLAM technology works similarly. It identifies landmarks, determines its position concerning those markers, and then continues exploring the designated area until it has enough landmarks to create a comprehensive map. This technology enables a device to map and locate a location simultaneously, like a person exploring a new environment.

Simultaneous, Localization & Mapping (SLAM): Applications

• Cleaning Robots

Cleaning robots are currently one of the most common applications of SLAM, despite their seemingly simple design. However, they are an excellent example of how Simultaneous, Localization and Mapping can work. Without SLAM, cleaning robots would move around randomly, colliding with obstacles and failing to remember where they have already cleaned.

With SLAM, these robots can map the area they need to clean, locate themselves, and navigate obstacles while tracking their progress. This ability to map and localize simultaneously makes cleaning robots efficient and effective. It is just one example of how SLAM technology can be applied in everyday situations, at home and beyond.

• Medicine

SLAM technology is making a significant impact in the medical field, particularly in the operating room, where it is aiding doctors in performing less invasive surgeries with greater ease and accuracy.

By utilizing SLAM technology, doctors can navigate the human body and quickly identify and analyze problems. It allows for minimally invasive surgeries, reducing the patient's trauma and providing more precise and efficient surgical procedures. Medical SLAM provides surgeons with a 3D model of the object inside the patient's body, allowing for a detailed and comprehensive view of the surgical area without requiring extensive incisions. This "bird's eye view" helps surgeons to operate with greater precision while minimizing the risk of complications.

• Entertainment

In December 2021, The Walt Disney Company was granted a patent for a cutting-edge "Virtual World Simulator" that relies on SLAM technology to create a seamless and immersive experience for park visitors. By constantly tracking the viewer's changing point-of-view, the Virtual World Simulator offers multiple users a dynamic 3D environment within a real-world theme park attraction - all without the need for glasses or a headset.

• Self-Driving Cars

Given that SLAM technology is primarily designed to help autonomous devices navigate unknown environments, it is no surprise that it closely ties to the development of self-driving cars. SLAM is the primary way autonomous vehicles find their way on the roads. By utilizing SLAM software, self-driving cars can identify everything from lane lines to traffic lights and other vehicles on the road. With greater accuracy and responsiveness than GPS technology, SLAM is poised to unlock the full potential of autonomous automobiles.

• Drones

To complete its intended operation successfully, an autonomous drone needs to know its location, map its surroundings, and plan a flight path dynamically in an ever-changing environment. It is where SLAM comes in.

Using LiDAR scanners and SLAM software, drones can alter their path and operation in real-time without manned intervention. Although some drones fly too fast for SLAM systems to accurately measure, a combination of LiDAR scanners and other imaging and mapping systems is leveraged.

The Future of SLAM (Simultaneous Localization & Mapping)

SLAM is becoming increasingly popular in robotics, particularly with the rise of indoor mobile trends. It provides an alternative to user-built maps, allowing robots to operate without a predefined localization infrastructure. Real-time parallel computation allows for potential data processing offloading to the cloud, providing significant performance gains. Modern point cloud registration algorithms that can run on distributed clouds unlock access to unlimited memory and CPU power, enabling the creation of maps in large and complex environments.

Conclusion

The emergence of self-driving cars has led OEMs to adopt the SLAM algorithm in manufacturing. Vehicles can gather data from sensors and cameras by leveraging localization and mapping techniques to provide an intelligent driving experience. As a result, the demand for the global computer vision market is expanding at a compound annual growth rate of 7% between 2022 and 2030. Consequently, industries require engineers with advanced skills to meet the growing demand for cutting-edge technology. Keeping up with the latest advancements in SLAM technology will be crucial in securing a foothold in this rapidly expanding field.

Skill-Lync offers a course on Localization, Mapping and SLAM course designed for students and graduates of computer science and related engineering. Talk to our experts to know more about it.