Definition: SLAM is a method in which a system determines its own position (localization) and creates a map of the environment (mapping) at the same time. With this technology, devices such as AR glasses, smartphones or autonomous robots can orient themselves in unknown environments. They do not depend on external positioning systems such as GPS.

The SLAM process runs in two parallel steps: while the system navigates through the environment, it identifies characteristic features (feature points) such as corners, edges and textures. At the same time, it uses these features to calculate its own movement and position. The system continuously compares the detected features with the growing map of the environment. This creates a precise spatial understanding.

Also known as: Simultaneous Localization and Mapping

SLAM in Augmented Reality

SLAM is the core technology behind markerless tracking in augmented reality. Modern AR frameworks such as ARKit (visual-inertial odometry) and ARCore (environmental understanding) use SLAM algorithms to position virtual objects stably in the real world, with no physical markers.

Visual-inertial odometry (VIO) combines camera data with inertial sensors (gyroscope, accelerometer) for more precise tracking. This fusion compensates for the weaknesses of individual sensors and enables robust SLAM, also during fast movements.

Feature-based SLAM detects distinct points in the environment and tracks their position across several camera images. Triangulation then reconstructs the 3D structure of the scene.

Technical Methods

Monocular SLAM Uses a single camera to capture the environment. Low cost, but prone to scale problems (scale ambiguity).

Stereo SLAM Uses two cameras for direct depth estimation. Higher precision in spatial reconstruction.

RGB-D SLAM Combines a color camera with a depth sensor (e.g. LiDAR). Delivers direct depth measurements and enables dense 3D reconstruction.

Visual-inertial SLAM Fuses camera data with inertial sensors. Standard in modern smartphones and AR glasses.

Use Cases

Augmented Reality Precise positioning of virtual objects in real environments without external markers. The basis for markerless AR applications.

Robotics and Autonomous Vehicles Navigation in unknown environments, obstacle avoidance, path planning.

Indoor Navigation Orientation in buildings without a GPS signal. Used in airports, shopping centers and warehouses.

3D Reconstruction Automatic creation of 3D models from camera images for architecture, historic preservation or surveying.

Challenges

Loop Closure Detection of places that the system already visited, to prevent mapping errors. Critical for long-term accuracy.

Dynamic Environments Moving objects (people, vehicles) can disturb the tracking. Robust algorithms must tell static features from dynamic features.

Computational Complexity Real-time SLAM requires considerable computing power. Optimization for mobile devices is challenging.

Environments with Little Texture Single-color walls or repetitive patterns make feature detection difficult. Modern systems use additional sensors (LiDAR) to compensate.

Industry Standards and Frameworks

ORB-SLAM Open-source SLAM system for monocular, stereo and RGB-D cameras. Widely used in research and development. Source: https://github.com/raulmur/ORB_SLAM2

ARKit (Apple) Proprietary SLAM framework with visual-inertial odometry for iOS devices. Source: https://developer.apple.com/augmented-reality/arkit/

ARCore (Google) Google's AR platform with environmental understanding and motion tracking. Source: https://developers.google.com/ar

OpenVSLAM Open-source visual SLAM framework that supports different camera types. Source: https://github.com/OpenVSLAM-Community/openvslam

Tracking technologies: - Markerless tracking – AR without physical markers - Marker tracking – AR with predefined reference points - Visual odometry – Position estimation from image sequences - LiDAR-based tracking – Tracking with depth sensors

AR technologies: - Augmented reality (AR) – Overlay of virtual content - Spatial mapping – Spatial capture of the environment - Plane detection – Detection of flat surfaces - Feature detection – Identification of distinct image points

Applications: - AR navigation – Wayfinding with augmented reality - 3D modeling – Digital 3D reconstruction - Robotics – Autonomous navigation - Mixed reality – Merging of real and virtual worlds