Simultaneous Localization and Mapping (SLAM)

SLAM—Simultaneous Localization and Mapping—lets a device such as a robot or AR (augmented reality) headset build a map of its surroundings and determine its own location within that map at the same time.

It gathers data from sensors like cameras, lidar, or IMUs (inertial measurement units), then continuously matches new observations to known features, updating the map and the device position using filtering techniques like particle filters or Kalman filters. This continuous process keeps the device aware of where it is, even in unfamiliar spaces, without relying on external references like GPS.

Find helpful insights about where AR is taking design and where designers might take AR in our article Augmented Reality – The Past, The Present and The Future.

SLAM solves two interlinked challenges in uncharted environments: mapping the space and tracking position within that map, both in real time. It enables devices without preloaded maps to navigate safely, avoid obstacles, and anchor virtual content accurately.

Applications for this include self-driving cars, drones, AR/VR systems, and robots working indoors or in GPS-denied spaces—empowering them with spatial awareness where external systems do not exist to be able to help.

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SLAM builds its own map of the environment and keeps track of position using onboard sensors. It works accurately indoors or in areas without GPS.

GPS, by contrast, retrieves location from satellite signals and does not create maps or work well indoors or in rapid motion or occlusion conditions. SLAM is self-contained—data comes from what the device sees or senses—while GPS depends on external infrastructure and supplies only coordinates, not an environmental model.

GPS, by contrast, provides location coordinates from satellite signals with accuracy typically ranging from 4.9 meters (16 feet) for smartphones under open sky to ≤1.82 meters (5.97 feet) for high-quality receivers. Even when GPS signals are available, GPS may perform poorly in indoor venues owing to signal attenuation, shadowing and multipath fading imposed by indoor concrete and metallic structures.

GPS is fundamentally limited by its coarse accuracy compared to local sensors—while enhanced GPS systems like RTK can achieve centimeter-level accuracy, standard GPS provides only meter-level positioning. Also, GPS supplies only coordinates without creating environmental maps, whereas SLAM is self-contained—generating both positional data and detailed environmental models from what the device directly observes.

Delve deeper into this fascinating area in our article Getting Lost and Found – Maps and the Mobile User Experience.

AR and MR rely on SLAM algorithms to track camera pose and map environmental features, enabling virtual objects to be spatially registered with the real world. SLAM systems identify keypoints and use sensor fusion to estimate depth and 3D structure, allowing digital content to appear anchored to surfaces and properly occlude behind real objects.

This spatial consistency is crucial for immersive experiences—and absolutely critical in medical applications where tracking failures could lead to dangerous misalignment between virtual surgical guidance and actual patient anatomy, for example—potentially giving “cutting edge tech” an unwanted meaning if unintentional contact were to be made.

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SLAM systems typically combine multiple sensors for accuracy. Popular choices include cameras (monocular, stereo, and RGB-D) for visual SLAM, lidar for precise depth mapping, and inertial measurement units (IMUs) for estimating orientation and motion. Some systems also use sonar, radar, or even Wi-Fi signal strengths. Sensor fusion helps the system remain stable in varied environments.

Discover additional aspects of augmented reality in our article Innovate with UX: Design User-Friendly AR Experiences.

Modern SLAM systems use specialized techniques to detect and manage dynamic elements to remain accurate. One common approach is dynamic object detection, where systems use visual cues, depth data, or semantic segmentation to identify and ignore moving objects.

Algorithms like DynaSLAM or Co-Fusion isolate static backgrounds while excluding dynamic regions from mapping. Sensor fusion—combining camera, IMU, and depth sensor data—helps track stable features while filtering out motion noise. Also, methods like RANSAC reject outlier points that behave inconsistently. Advanced systems also apply semantic SLAM, using AI to recognize object types (e.g., people or vehicles) and handle them appropriately. These innovations let SLAM function effectively in crowded or changing environments, supporting robust AR, autonomous navigation, and robotics in real time, an essential, forward-thinking design consideration that helps keep SLAM relevant to user needs and not the sound of a collision.

Get a greater grasp of how to accommodate user needs in design.

Designers can leverage the spatial maps of SLAM to attach virtual UI elements to physical surfaces. For instance, surfaces can become interactive touchpoints or anchors for virtual controls. To make interactions natural, designers should prioritize surface detection, ensure object occlusion so that virtual elements appear behind real-world objects when appropriate, and provide visual consistency when users move or reorient view. These practices reinforce spatial cohesion and boost immersion to levels that can safely delight.

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When SLAM underperforms, virtual objects may drift, float, or jitter. They may scale inconsistently or detach from real surfaces. These issues damage immersion and cause user frustration—misaligned content can appear unnatural, triggering motion sickness or breaking interaction patterns. Depending on the type of AR experience or consequences of actions taken on a misaligned object, results can be more damaging if something is there that is not supposed to be. Stable SLAM is essential for believable, comfortable AR experiences that do not end in problems.

Get more from AR design in our article How to Design for AR Experiences on the Go.

Designers and developers face several SLAM challenges: high processing demands can drain batteries and slow performance; poor lighting or texture-less surfaces can degrade tracking; dynamic environments introduce moving occlusions; and sensor limitations or calibration issues can create drift or map inconsistencies.

Ensuring cross-device compatibility adds complexity—especially on mobile hardware with varying capabilities—and is another reason that the application of SLAM requires careful thought for applications users can enjoy in comfort.

Get a firmer foundation in the fineries and theoretical aspects to help build better SLAM-oriented experiences, in our article Beyond AR vs. VR: What is the Difference between AR vs. MR vs. VR vs. XR?.

SLAM accuracy depends on sensor quality, algorithm robustness, and the consistency of the environment. Rich textures and structured environments (walls, distinct features) improve feature matching and mapping. High-quality cameras or lidar boost accuracy, helping matters greatly.

Meanwhile poor lighting, repetitive patterns, or dynamic scenes harm accuracy. Loop closure techniques—recognizing previously visited locations—can correct positional drift over time.

Find helpful insights about spatial design the AR way in our article Spatial UI Design: Tips and Best Practices.

Yes, modern mobile devices such as AR-capable smartphones and wearables integrate SLAM with real-time performance. They combine cameras, IMUs (inertial measurement units), and efficient Visual SLAM algorithms to map indoor spaces and support immersive AR experiences.

These on-device implementations enable real-time interaction—however, note that performance depends on hardware specs and optimization.

Go further into AR and consider important aspects about how to design for it, in our article Cross-Device AR: How to Ensure a Seamless Experience.

Jakl, A. (2018, September 21). Basics of AR: SLAM – Simultaneous localization and mapping. Andreas Jakl Blog. https://www.andreasjakl.com/basics-of-ar-slam-simultaneous-localization-and-mapping/

This detailed technical blog post explains SLAM concepts specifically within the context of ARKit, ARCore, and HoloLens implementations. Jakl breaks down complex algorithms into understandable components, explaining feature extraction, keypoint tracking, loop closure detection, and uncertainty handling in SLAM systems. The post addresses practical limitations UX designers encounter, such as drift correction, tracking loss, and environmental requirements for reliable SLAM performance. This resource helps UX designers understand why AR applications behave certain ways and how to design experiences that work within SLAM technical constraints, making it invaluable for evidence-based AR design decisions.

Wilson, T. (2018, June 15). The principles of good UX for augmented reality. UX Collective. https://uxdesign.cc/the-principles-of-good-user-experience-design-for-augmented-reality-d8e22777aabd

This practical design article addresses UX principles specifically for AR applications, with particular attention to spatial interaction challenges that SLAM technology presents. Wilson discusses comfortable interaction zones, spatial UI placement, and how tracking limitations affect user interface design decisions. The article provides actionable guidance on designing within SLAM constraints, considering device ergonomics and tracking accuracy when placing virtual objects in real space. For UX designers, this resource offers concrete design recommendations for creating usable AR experiences that account for SLAM tracking limitations and spatial computing.

Tourani, A., Bavle, H., Sanchez‑Lopez, J. L., & Voos, H. (2022). Visual SLAM: What Are the Current Trends and What to Expect? arXiv.

Tourani and colleagues survey state-of-the-art VSLAM systems, covering algorithm types, sensor variants (monocular, stereo, RGB‑D), and performance across real-world datasets. UX designers can use this knowledge to set expectations for visual tracking, environment diversity, and device limitations in AR/MR interfaces.