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SLAM stands for simultaneous localization and mapping. It lets a robot estimate where it is while building a representation of the environment around it, particularly when no reliable map exists beforehand. The robot combines motion estimates with sensor observations, identifies previously seen places to limit drift, and produces a map that other navigation components can use.
Localization answers “Where am I?” Mapping answers “What is around me?” SLAM estimates both at once because each problem helps solve the other. This guide explains the idea, the sensors and algorithms involved, a practical ROS 2 workflow, common failure modes, and situations where a different positioning method is more appropriate.
How SLAM works
A mobile robot continually receives two kinds of information:
- Motion information: odometry or another estimate of how the robot moved since its last measurement.
- Environmental observations: laser ranges, camera images, depth measurements or other sensor readings that describe nearby features.
SLAM uses both streams to estimate a changing robot pose and a map in a shared coordinate frame. For a basic ground robot, pose includes two-dimensional position and heading. More advanced systems can also estimate velocity, inertial-sensor biases, camera parameters and calibration values.
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The estimates are coupled. If the robot’s pose is wrong, observations may be placed in the wrong map location. If the map is wrong, later observations can be matched to the wrong places and pull the pose estimate off course. A SLAM system therefore maintains uncertainty and repeatedly updates both estimates as new data arrives.
Dead reckoning and accumulated drift
Odometry alone is a local estimate. Wheel slip, uneven floors, encoder quantization, timing errors and imperfect motion models cause small errors that accumulate over distance. A robot can appear to follow a consistent path while its calculated position gradually separates from its true position.
SLAM limits that drift by matching current observations with features or places already represented in the map. When the system recognizes a previous location, it can add a constraint between the current pose and the earlier pose. This event is called a loop closure. The system then adjusts a set of poses, landmarks or both so the overall map is more globally consistent instead of preserving every accumulated odometry error.
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The word “map” does not imply one universal data format. Depending on the robot and task, it can contain:
- Occupied and free cells in a two-dimensional occupancy grid.
- Geometric landmarks such as points, lines or planes.
- Three-dimensional points, surfaces or volumetric cells.
- Visual keyframes and feature descriptors used for recognizing places.
- A topological graph showing how rooms or locations connect.
A map is useful only in the coordinate system and resolution required by the next task. A delivery robot may need a navigable 2D obstacle map, while an aerial or manipulation system may require a detailed 3D representation.
Mapping versus localization
Mapping estimates the environment from sensor observations. Localization estimates the robot’s pose relative to an existing map or reference. SLAM performs the two operations together when the map is unknown, incomplete or not trustworthy.
They are not interchangeable modes. A robot that already has a verified map can often localize against it without rebuilding the map. Conversely, a mapping run may produce a map that is later used by a separate localization system. If an external positioning source such as GPS or known artificial beacons provides reliable global position, simultaneous map construction may add unnecessary complexity.
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When a prior map is enough
Use map-based localization instead of SLAM when the environment is stable, the existing map is accurate enough for the task and the robot can observe distinctive features or landmarks that tie its pose to that map. This is common in repeatable industrial routes and other spaces with controlled geometry.
When SLAM is the better fit
SLAM is valuable when a robot must explore an unfamiliar indoor space, when GPS is unavailable or unreliable, or when the available map is incomplete and must be built during operation. It can also reveal the topology of a place for later planning or give a human operator a live spatial view.
Core stages inside a SLAM system
Implementations differ, but most SLAM pipelines contain several conceptual stages.
1. Acquire and time-stamp sensor data
Laser scans, camera frames, depth images and inertial measurements must have reliable timestamps. The system needs to know which measurements correspond to one another and where each sensor is mounted relative to the robot body.
2. Estimate short-term motion
Wheel encoders, inertial sensors, visual odometry, lidar odometry or a fused source provide a prediction of the next pose. This prediction is useful even though it drifts, because it narrows the search for matching observations.
3. Extract or register observations
A laser system may align scan geometry. A visual system may detect and track image features. A depth system can register three-dimensional points. The result is a measurement relating the current observation to a previous pose or landmark.
4. Update the state and map
The estimator combines the motion prediction and observation constraints. Some systems use probabilistic filters; others optimize a graph of poses and measurements. The map is updated in the same coordinate frame as the robot state.
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5. Detect and solve loop closures
Place recognition searches for evidence that the robot has returned to an earlier location. A verified match adds a long-range constraint. Graph optimization or another global correction then distributes the adjustment through the trajectory rather than leaving a kink at one point.
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Sensor choices: lidar, cameras and inertial measurements
There is no universal sensor that makes SLAM best for every robot. Select a method based on the environment, required map dimension, available compute, calibration effort, ROS 2 drivers and whether a known map or external positioning source already exists.
| Approach | Primary observations | Typical fit | Important considerations |
|---|---|---|---|
| 2D laser SLAM | Planar laser range scans, commonly combined with odometry | Indoor or other environments where a two-dimensional occupancy map is sufficient | A documented ROS 2 route uses SLAM Toolbox with a compatible sensor_msgs/LaserScan stream. Frame IDs, scan topic, mounting geometry and odometry must be configured correctly. |
| Visual SLAM | Monocular, stereo or RGB-D camera images | Robots that already carry cameras and operate in scenes with trackable visual features | Camera model, exposure, texture, motion blur and lighting affect tracking. ORB-SLAM3 supports monocular, stereo and RGB-D cameras with pinhole and fisheye models. |
| Visual-inertial SLAM | Camera observations plus inertial measurements | Systems seeking visual tracking supplemented by acceleration and rotation data | Camera–IMU calibration, timestamp synchronization and sensor placement are part of setup. An IMU does not automatically remove every visual-tracking failure. |
Lidar is therefore optional, not a requirement for navigation or SLAM in general. A camera or depth-based system may be more suitable when visual information is plentiful, while laser data can be attractive when geometric range measurements are easier to interpret than appearance.
Questions to answer before choosing a sensor path
- Do you need a 2D map, a 3D map, or only a pose estimate?
- Are surfaces textured and well lit enough for camera features?
- Will dust, darkness, glare, glass or repetitive structure degrade one sensing modality?
- What compute, bandwidth and storage can the robot provide?
- Is a maintained driver available for the robot’s ROS 2 distribution?
- Can you measure the sensor extrinsics and keep all devices time-synchronized?
Published system results are tied to particular sensors, environments and evaluation protocols. The available documentation does not establish a universal accuracy ranking between these approaches.
ROS 2 example: building a map with SLAM Toolbox and Nav2
The current Nav2 Rolling documentation uses SLAM Toolbox as an example mapping component. This is a version-specific walkthrough: Rolling documentation and package interfaces can change, so use the branch that matches the ROS 2 distribution installed on your robot.
What the system must provide
- A running robot interface with the appropriate sensor and motion topics.
- A compatible
sensor_msgs/LaserScantopic. Nav2 documentation conventionally uses/scan, but the topic name is configurable and must match the actual driver. - Correct frame IDs and transforms from the robot body to the sensor.
- Odometry that publishes the robot’s local motion relationship.
- Nav2 and SLAM Toolbox installed for the selected ROS 2 distribution.
The frame relationship
Nav2 describes the normal transform chain as:
map → odom → base_link → sensor frames
SLAM supplies the map to odom transform and publishes the /map representation. The odometry source supplies odom to base_link. Odometry can come from wheel encoders, an IMU, visual odometry, lidar, radar or a sensor-fusion system.
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Launch sequence
- Bring up the robot interfaces. Start the base driver, sensor driver and odometry source. Confirm that the expected scan and transform topics are actually publishing.
- Start Nav2 without static-map localization components. A live SLAM run builds the map, so do not start the mode intended solely to localize against an already saved static map.
- Launch SLAM Toolbox. Configure its scan topic, frame names and timing to match the robot rather than copying another robot’s parameters unchanged.
- Inspect the map and transforms in RViz. Verify that scan data overlays the expected walls and obstacles, that the robot moves in the correct direction, and that the transform tree remains connected.
- Drive deliberately through the space. Keep enough overlap between successive observations for scan matching or visual tracking. Revisit distinctive areas so the system has an opportunity to detect loop closures.
- Save the resulting map with
map_saver_cli. Treat the saved file as an artifact of this run and record the ROS 2 distribution, sensor configuration and map resolution used to create it.
The Nav2 tutorial expects robot-specific interfaces to be substituted. A launch file or parameter set from one platform is not a drop-in configuration for another: topic names, frame IDs, sensor extrinsics, wheel geometry and time behavior all matter.
Checks before blaming the SLAM algorithm
- Inspect the scan topic and confirm each message has valid ranges and the expected frame ID.
- Check that the sensor frame transforms to
base_linkwith the correct translation and rotation. - Verify that timestamps advance consistently and that sensor and odometry clocks are not offset.
- Confirm that the odometry transform has the expected direction and sign conventions.
- In RViz, look for scans that jump, rotate with the wrong axis or appear far from the robot.
- Make sure only the intended node publishes each required transform.
How to judge a mapping run
A visually complete map is not automatically a reliable one. Review the run against the job the robot must perform.
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Geometry and consistency
Walls that should be straight should not appear as doubled or widely separated lines. A corridor revisited after a loop closure should align with its earlier representation. Check whether the robot’s displayed pose remains plausible when it crosses the same doorway or landmark.
Coverage and observability
Unseen areas remain unknown. Feature-poor rooms, long blank corridors and highly repetitive shelving provide fewer constraints. Plan a route that exposes corners and distinctive structures instead of racing through the center of open spaces.
Dynamic objects
People, forklifts, doors and movable furniture do not belong in a permanent static map. If a moving object is repeatedly observed, the system can confuse it with a landmark or obstacle. Map during a representative quiet period, and configure downstream obstacle handling to deal with objects that change after mapping.
Long-term behavior
A map can age as furniture, construction or lighting changes. Robustness and scalability in long-term mapping remain active engineering challenges. Store the conditions and configuration for each map so a later operator can determine whether a mismatch comes from environmental change or a localization error.
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The map bends, stretches or slowly rotates
This usually indicates accumulated odometry error, weak observation constraints or missing loop closures. Improve wheel calibration, reduce wheel slip, check timestamps and drive routes that revisit recognizable structure. A loop closure can correct global drift only when the system recognizes and verifies the return.
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Scans appear detached from the robot
Inspect the static transform between the sensor frame and base_link. A wrong frame ID, incorrect sensor orientation or a stale transform can make a healthy SLAM algorithm look broken.
The robot moves but the map does not update
Confirm that SLAM is subscribed to the actual scan topic, that messages use the expected type, and that the transform from the sensor to the robot is available at each scan timestamp. Also check whether a lifecycle-managed node is still inactive.
Visual tracking repeatedly drops
Look for low texture, darkness, glare, motion blur and rapid camera motion. Improve illumination or camera settings where possible, slow the robot, verify the camera model and check camera–IMU calibration and synchronization in a visual-inertial setup.
A loop closure produces a sudden incorrect jump
The place-recognition match may be false, especially in repetitive environments. Review the candidate match and geometric verification settings, and avoid treating a single visually similar corridor as a unique landmark without additional constraints.
Navigation fails even though the map looks correct
SLAM is only one part of the navigation stack. Nav2 still needs valid odometry, transforms, planners, controllers, costmaps and obstacle data. A map publisher does not by itself make the robot safe or capable of reaching a goal.
Requirements checklist for a dependable deployment
- Defined goal: decide whether you need live exploration, a reusable map, localization on a fixed map or only short-term pose tracking.
- Suitable sensing: choose laser, camera, depth, inertial or a combination based on environmental observability.
- Mechanical calibration: measure wheel geometry, sensor mounting and any relevant offsets.
- Time discipline: synchronize sensor and odometry timestamps and investigate dropped or delayed messages.
- Frame discipline: maintain one coherent transform tree with the conventional
map,odom,base_linkand sensor frames where appropriate. - Compute margin: leave capacity for mapping, navigation, logging and perception at the required update rates.
- Operational plan: control speed, revisit useful landmarks and account for people or other moving objects.
- Validation: test the saved map and localization behavior on routes that were not used only once during mapping.
When not to use SLAM
SLAM adds estimation, mapping, storage and data-association work. It is a poor fit when a simpler, better-characterized reference is available. Examples include:
- A robot operating in a stable facility with a trusted prebuilt map and reliable map localization.
- A vehicle with dependable GPS or another external positioning system over the entire operating area.
- A machine that only needs relative motion over a short interval and does not need a persistent map.
- A platform whose sensors cannot observe enough stable structure for the chosen SLAM method.
In those cases, use the positioning method that meets the accuracy, availability and safety requirements, and reserve SLAM for environments where simultaneous estimation genuinely solves a problem.
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SLAM software choices in context
“SLAM” describes a problem and a family of methods, not one product or algorithm. Implementations include probabilistic filtering, graph optimization, feature-based visual methods and combinations of these approaches. Compare candidates on the dimensions that affect your robot:
| Decision factor | Questions to ask |
|---|---|
| Environment | Are scenes textured, geometric, repetitive, dynamic, dark, dusty or reflective? |
| Map dimensionality | Will a planar occupancy grid support navigation, or is a 3D representation required? |
| Sensor and driver support | Does the implementation accept the message types and camera or lidar models provided by your ROS 2 drivers? |
| Calibration burden | Can the team measure extrinsics and maintain synchronization over time? |
| Compute and latency | Can the processor sustain tracking, loop closure and navigation simultaneously? |
| Operational duration | Will the robot map for minutes, hours or repeated long-term missions? |
| Existing references | Would a known map, beacons or external positioning make simultaneous mapping unnecessary? |
ORB-SLAM3 is one visual and visual-inertial implementation; its project materials describe support for monocular, stereo and RGB-D cameras and pinhole or fisheye models. Its repository lists GPLv3 licensing and directs users to contact the authors regarding a closed-source commercial version. Treat those licensing terms separately from the technical question of whether its sensing assumptions fit your robot.
Quick Recap
Key takeaways
- SLAM estimates a robot’s pose and an environment map together when a reliable prior map is unavailable.
- Motion estimates drift; recognizing a previously visited place adds a loop-closure constraint that can improve global consistency.
- Lidar is optional. Two-dimensional laser SLAM, visual SLAM and visual-inertial SLAM each make different assumptions about sensing and calibration.
- In the Nav2 model, SLAM publishes
/mapandmaptoodom; odometry suppliesodomtobase_link. Planning and obstacle handling remain separate components. - Correct topics, frames, extrinsics, timestamps and robot-specific drivers matter as much as the choice of SLAM package.
- If a known map or dependable external positioning already solves localization, building a map at the same time may not be necessary.
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