How SLAM enables real-time tracking of virtual objects

Key Takeaways:

• Simultaneous localization and mapping (SLAM) is a fundamental technology in augmented reality (AR). It enables devices to understand their surroundings, track their position, and accurately place virtual objects in the real world.

• The SLAM process in AR includes setting up the virtual environment, sensor detection and feature extraction, SLAM localization and mapping, object detection and tracking, anchoring virtual objects, rendering and displaying, and interaction.

• By accurately understanding the environment and tracking the device's position, SLAM allows for seamless integration of virtual objects into the real world, making AR experiences more immersive and interactive.

SLAM (simultaneous localization and mapping) is a technology for devices to track the position of surrounding objects while simultaneously keeping track of their own position on the map. SLAM incorporates computer vision, enabling devices to perceive and interpret surrounding visual information. Virtual objects are digitally created or computer-generated entities that do not have a physical presence. We’ll discuss how augmented reality (AR) uses SLAM to detect digitally generated entities in the virtual world.

Real-time tracking with SLAM in AR

The process of incorporating SLAM with AR helps in detecting objects in augmented reality. There are certain steps to the process that include setting up the environment to detect and visualize the objects in virtual reality. This process dynamically determines the viewer’s orientation in relation to the objects present in the surroundings. The steps in the process include:

Setting up the virtual environment

• Sensor detection: AR uses various sensors, such as cameras and IMUs (Inertial Measurement Units) that combine accelerometers, magnetometers, and gyroscopes. These sensors facilitate accurate result production and error correction while tracking the user's head movement. AR also uses depth sensors, such as time-of-flight (ToF), to calculate the distance from objects.

• Feature extraction: Computer vision algorithms extract features from the collected data. These features are reference points for movement, object recognition, and tracking. Each feature tracked receives a feature descriptor with basic information about the feature.

SLAM localization and mapping

• Visual SLAM: In feature tracking, SLAM algorithms track features as the devices move. The features tracked are used to determine the orientation and position of the device. During the process of determining the orientation, visual SLAM constructs a map of the environment. The map identifies the features and updates in real time as the device moves.

• Sensor fusion: SLAM combines data from various sensors to enhance detection accuracy. Data fusion helps AR handle diverse environmental conditions and device movements.

• Loop closure: This feature allows the visual SLAM to keep track of the visited tracks, locations, and areas on the maps. Sensor noise can cause ambiguous data output. The loop closure data helps detect revisits to a location to reduce ambiguity.

• Semantic SLAM: In advanced SLAM systems, semantic data adds further meaning and understanding to the type of feature and object detected in the environment. This enhances AR’s capability to understand the scene better.

Object detection and tracking

• Feature matching: This process involves matching the identified visual features of the objects to the database of real-world object features. It involves techniques like SIFT Scale-Invarient Feature Transform is a computer vision technique that identifies keypoints from images for easier object recognition.and SURFSpeeded-Up Robust Features is a computer vision technique for detecting keypoints in images. It has scaling, rotating, and illumination capabilities for easier vision tasks. to compare key features and match them with reference features.

• Pose estimation: Pose estimation helps recognize the position and orientation of the identified features within the environment.

• 3D object detection: In cases where 3D objects need to be detected, a 3D model detection method is deployed. Depth sensors, like stereo vision systems, provide in-depth information about the 3D pose of the object.

• Machine learning algorithm: The object recognition system uses machine learning models and techniques, such as deep learning and neural networks, to recognize objects in images and videos. The deep learning algorithm identifies objects in complex scenarios with high accuracy.

Once the object is recognized, its pose is detected. The tracking algorithms continuously monitor the movement with the change in orientation of objects and the device orientation. The tracking algorithms commonly used are Kalman filtersMathematical algorithm that uses series of measurements to detect the state of dynamic system. or particle filtersA probabilistic technique that uses set of partcles to represent possible states of the system. The weight of the particles is updated based on system's state..

Anchoring virtual objects

• Coordinate transformation: Coordinate transformation algorithms anchor the coordinates of virtual objects to real-world objects. These algorithms are mathematical operations that alter and rotate the coordinates to match real-world objects’ coordinates.

• Surface placement: After successfully detecting the object, it is important to find the surface on which to place it. The algorithms align the virtual objects with the real-world surface.

Render and display

• 3D rendering: The 3D objects detected are displayed on a 3-dimensional surface. Light, shadows, and material properties are considered to create a realistic appearance. Material for objects considers properties like transparency and texture. The rendering algorithms ensure that the objects have the properties to make them more realistic.

• Alignment with the user: It is important to align the objects with the user’s point of view, so they appear correctly positioned in the environment.

Interaction

• Gesture recognition: The algorithms enable the AR system to recognize gestures such as swiping, punching, or moving. The recognition of gestures helps the device perform actions in the virtual environment. The actions result in manipulating and changing objects within the AR environment.

• Simulation: Physical simulation algorithms are deployed to make virtual objects interact with the device. The simulation allows following the principles of physics, like friction and gravity, for a more realistic AR experience.

Real-time updating

SLAM algorithms continuously update the device’s orientation and position relative to the actions and the newly collected sensor data. This data helps in the alignment between the virtual and real-world elements.

Occlusion handling

OcclusionThe situation where an object is obsecured or hidden by the presesnce of another virtual object. handling is a technique to ensure correct orientation by emitting the possible occlusion in the environment. The occlusion culling technique helps detect occlusion relative to the user’s viewpoint. Also, collision detection techniques are applied to prevent virtual objects from intersecting with real objects.

It is important to note and keep track of the movements in the AR environment. Once the frontend 3D objects, orientation, and the map are identified, establishing relationships between objects and changing orientation and pose with environmental changes is crucial. These steps must be followed to track and manage the orientation and object changes in the AR environment.