Which algorithms are commonly implemented for SLAM in AR?

SLAM (Simultaneous Localization and Mapping) in AR commonly relies on algorithms that balance real-time performance with accuracy, given the constraints of mobile hardware. Key algorithms include feature-based methods like ORB-SLAM, direct methods like LSD-SLAM, and sensor-fusion approaches such as visual-inertial odometry (VIO). These algorithms enable AR devices to map environments and track their position within them, which is essential for overlaying digital content consistently in the physical world. Each approach has trade-offs in computational efficiency, robustness, and map density, making them suitable for different AR use cases.

ORB-SLAM is a widely used feature-based algorithm that extracts ORB (Oriented FAST and Rotated BRIEF) features from camera frames to build sparse maps. It operates in three parallel threads for tracking, mapping, and loop closure detection, which helps correct drift over time. For example, ORB-SLAM2 supports monocular, stereo, and RGB-D cameras, making it adaptable to AR devices with varying sensor setups. However, its reliance on sparse features can limit its ability to handle textureless surfaces. In contrast, LSD-SLAM (Large-Scale Direct SLAM) uses direct methods, processing pixel intensity data to create semi-dense maps. This approach works better in low-texture environments but requires more computational resources, which can challenge mobile AR devices. Both algorithms are open-source, allowing developers to customize them for specific AR applications.

Industry frameworks like Google’s ARCore and Apple’s ARKit abstract SLAM implementation but rely on underlying algorithms optimized for mobile hardware. ARCore uses VIO, combining camera data with inertial measurements from a device’s IMU (accelerometer and gyroscope) to improve tracking stability. ARKit employs similar techniques, leveraging the iPhone’s LiDAR scanner in newer models for depth sensing and dense mapping. These frameworks prioritize real-time performance and power efficiency, often sacrificing map detail for smoother AR experiences. For developers building custom solutions, hybrid approaches—such as combining feature-based tracking with inertial data—are gaining traction. Additionally, advancements in machine learning, like semantic SLAM, are being explored to enhance environment understanding, though these methods are not yet mainstream due to computational demands. Choosing the right algorithm depends on factors like device capabilities, environmental conditions, and the level of detail required for the AR application.