What is Simultaneous Localization and Mapping (SLAM)?
Simultaneous Localization and Mapping (SLAM) is the task of computing the position and orientation of a mobile platform (robot or sensor) as it moves through an environment while simultaneously building a map of that environment. So, this technology solves this by constantly updating both the map and the robot’s location using data from cameras, LiDAR, radar, and other sensors. Let's dive into slam-based practices used for As-Builts and beyond.
Have I Seen SLAM in My Life?
Most likely yes, and it's more popular than you think. SLAM algorithm has been very successful and has developed a lot in the last 10 years. Without SLAM your vacuum robot wouldn't be able to clean the unknown environment, localize the base, or use any navigation system at all. Using SLAM technology and artificial intelligence is a necessity to map an unknown environment. You have seen this technology in a number of fields: autonomous cars, vacuum cleaners, drones, delivery robots, and so on. This list will grow as the SLAM technology will continue to develop.
How can SLAM be used in Architecture?
One of the older techniques we have seen in the field is mapping the environment by drones. Scanning a landmark of any size becomes easier and more affordable, saves lots of man-hours, and provides higher accuracy. Like a Roomba sweeps your floors, drones can create a map by flying over points of interest, measuring the environment around, and providing dimensional data within the map.
One of the newer uses is the SLAM HD scanner. A scan technician can enter an unknown environment while simultaneously light detection and ranging will collect the data. That can be done by using a robot as a vehicle to map and attaching LiDAR scanners. There's also another type of SLAM when the machine is worn by the technician who will carry it through the space to be measured. Built-in LiDAR laser scanner technology allows precise measurements, while the scanner can develop a map of an unknown environment. Data acquisition is done by cameras in 2D and 3D, making the scanning survey quick and accurate. LiDAR SLAM is commonly used to measure large objects and structures in a timely manner while focusing on accuracy.
Other applications of SLAM Technology
Autonomous Vehicles – Real-Time Perception and Navigation
To navigate safely in the real world, self-driving cars require an accurate real-time perception of the world around them. SLAM leverages several sensors, such as LiDAR, cameras, radar, and IMUs (Inertial Measurement Units) to provide a map and estimate the vehicle’s position in it, while continuously maintaining a high-fidelity map.
How SLAM Improves Autonomous Driving:
Dynamic object detection. Recognizes pedestrians, cyclists, and vehicles in real-time.
Lane-Level Localization. Determines location accurately even in challenging environments with poor GPS reception, including dense urban areas and tunnels.
Predictive Path Planning. Reads the road ahead and changes the route given map updates in real-time.
Sensor Fusion for Redundancy. Combines data from different sensors to compensate for the fact that an isolated system could fail.
Robotics – Industrial Automation, Aerial Drones, and Last-Mile Delivery
SLAM is the backbone of modern robotics, enabling machines to operate autonomously in structured and unstructured environments.
Industrial robotics and warehousing
Automated Guided Vehicles(AGVs) and Autonomous Mobile Robots(AMRs) deployed in factory floors or warehouses navigate using SLAM to deliver goods from one point within the facility to another with minimal or no human intervention.
SLAM-enabled robotic arms are used for tasks such as precision assembly, quality inspection, and pick-and-place operations.
Drones and Unmanned Aerial Vehicles (UAVs)
SLAM-enabled drones can achieve autonomous navigation where GPS is unavailable. This is important for drone navigation in GPS-denied environments such as indoor inspections, disaster response applications, and military surveillance.
Multi-sensor fusion helps the stability of a drone by combining IMUs, LiDAR, and Visual sensors to perform SLAM within drones that fly in turbulent high-wind or low-light environments.
Autonomous Delivery and Smart Mobility
Delivery robots leverage SLAM to map out a map of the sidewalks, where their mobile robot drives along while detecting pedestrians and arriving to their assigned destination efficiently.
SLAM's flexibility enables robots to operate in constantly changing settings, learning and adapting from obstacles they face.
3D Mapping & Surveying – High-Resolution Spatial Data
SLAM has transformed the way we see and understand rapidly our built environments replacing traditional surveying work in favour of real-time 3D mapping.
Key Applications
Major Applications in Construction & Real Estate LiDAR SLAM for Large-scale Mapping — applicable in Infrastructure planning, topographic mapping, and site analysis. Point clouds of whole cities, interstates, highways, construction sites.
Handheld SLAM Scanners – Handheld scanners are portable devices that generate as-built documentation by scanning internal spaces for purposes such as planning renovations, real estate evaluation, and facility management.
Digital Twin Integration – Point clouds generated by SLAM can be directly incorporated into BIM workflows, enhancing design precision and minimizing expensive rework.
Healthcare & Medical Robotics - Surgical Precision and Autonomous Patient Tracking
SLAM technology is advancing healthcare through surgical robotics, assistive robotics, and autonomous hospital robots:
Surgical Robots – Da Vinci Surgical Robot used SLAM to achieve sub-millimeter precision in minimally invasive surgery.
Autonomous Hospital Assistants - Powered with SLAM, these robotic helpers navigate hospital corridors, ferrying supplies and providing assistance to healthcare staff.
Wearable SLAM for Rehabilitation: Motion-tracking exoskeletons that detect SLAM movement to adapt and reconstruct the environment according to the user’s motion. Used in the rehabilitation of stroke patients of those with mobility impairments.
LiDAR SLAM
LiDAR SLAM is a powerful method for real-time mapping and autonomous navigation. Sensors emit laser beams and measure the time it takes for these beams to return after hitting an object. This allows for the creation of dense 3D point clouds representing the surrounding environment. Unlike cameras that primarily capture visual (2D image) information, LiDAR provides direct depth measurements, offering a geometric understanding of the environment.
Advantages
Unmatched accuracy. Sensors measure distances with centimeter or even millimeter-level accuracy, making them ideal for applications where precision is critical.
Works in All Lighting Conditions. LiDAR does not depend on light conditions. It can work in utter darkness, direct sunlight, fog, and low-visibility environments.
Handles Complex and Large-Scale Environments. LiDAR SLAM creates high-resolution maps for entire cities, forestlands, construction sites, and underground tunnels. The ability to scan such large areas with impressive accuracy supports infrastructure inspection, urban planning, and environmental monitoring.
Fast Real-Time Processing. High-end systems can rapidly process millions of points every second—fast enough to enable real-time navigation even in high-speed applications, such as self-driving cars and aerial drones.
While LiDAR SLAM offers impressive accuracy, it also presents some challenges. The vast amount of data generated demands substantial computing power to process in real time. Sensors of high quality can be costly, limiting their use in more affordable, consumer-grade applications. Additionally, highly reflective or transparent surfaces can occasionally disrupt measurements, necessitating sophisticated filtering and sensor fusion strategies to ensure precision.
Even with these problems, LiDAR SLAM continues to be regarded as the best of the algorithms for applications where accuracy is paramount. In self-driving cars, it ensure autonomous vehicles can reliably detect road edges, pedestrians, and obstacles. In warehouse automation, it enables robots to reliably and efficiently navigate and operate in their evolving environments. In aerial mapping, drones equipped with sensors can build high-resolution 3D models of cities, landscapes, and construction sites.
BIM definition lies in INFORMATION and should improve the construction operations building information exchange. All the data can be used in any CAD software. The main goal is building information management and coordination between the parties and any 3D CAD program will do the trick as long as everyone agrees on a specific software. The BIM environment has grown tremendously and if you're ready to embrace it - we're here.
SLAM Measurements for Construction
Autonomous robots are gaining more and more demand in construction, as they help optimize labor costs and improve the quality of work. One of the most important areas of application is the automated collection of data and monitoring of construction sites – this is necessary for creating relevant BIM models and digital twins of facilities.
SLAM technology is revolutionizing the measurement, mapping, and analysis of commercial buildings. Rather than static measurements and manual data collection of traditional surveying technology, SLAM allows for real-time, automated mapping of complex interior spaces.
This enables architects, engineers, and facility managers to quickly and easily create accurate 3D models and digital twins of complex structures with minimal disruption to operations.
SLAM works by using sensors—LiDAR, cameras, and inertial measurement units (IMUs)—to track movement and create a dynamic map. As the scanner moves through a building, it continuously updates its position and refines the model. This ensures accurate and high-resolution 3D point clouds that can be converted into as-built drawings, BIM models, or CAD files for further use in design, construction, or facility management.
In comparison to manual measurement techniques or static laser scanning, these systems present several important benefits. They support a much faster data capture process, mapping large commercial spaces in minutes as opposed to hours. The mobility of a SLAM scanner makes it possible to map a hierarchical space without having to waste time re-positioning the scanner. SLAM systems are also dynamic, meaning they are able to map a space even if people or objects are moving around.
Major Challenges in SLAM
Data Association Errors
SLAM must accurately associate sensor data taken at different times with the map, and failure to do this can cause the map to be deformed. This is particularly difficult in environments with many repeating structures, such as long corridors, or for fast movement, such as in dynamic environments and in situations where lighting changes. To fix this, SLAM systems use advanced feature-matching techniques.
Loop Closure Errors
When a robot returns to a previously visited area, the algorithm should detect that it is in a loop and update the existing map. Failure to detect such a loop leads to a compounding error over time, introducing drift into the map, which makes the map inconsistent with the surrounding environment and therefore unreliable for navigation. This is a significant problem in environments with repeated structures, such as in closed environments like office buildings, subway systems or mines and tunnels. Systems use different sensors and different techniques for loop closure detection.
Dynamic Environments
SLAM assumes that the world around it is static, but the real world is full of moving objects. Pedestrians, cars, and moving objects can confuse SLAM systems. SLAM maps these temporary features as static and adds them to a map. To address this issue, detection and tracking of moving objects (DATMO) have been added to SLAM systems.
GPS-Denied Environments
SLAM is often used in GPS-denied environments. In underground tunnels, underwater, indoors, and in canyons the satellite signals are blocked and SLAM has to rely on its on-board sensors alone. Without GPS, errors inevitably accumulate more quickly and make it more difficult to reliably navigate.
To solve this problem, SLAM uses feature-based mapping, which relies on environmental details instead of external positioning. Advanced technology generates detailed 3D maps, helping maintain accurate localization.
SLAM is a powerful technology, and with advancements in sensor fusion, deep learning, and optimization algorithms, this technology is becoming more accurate, scalable, and robust for applications in autonomous driving, robotics, and augmented reality.
