SLAM stands for simultaneous localization and mapping. It lets a robot estimate where it is while it builds a map of the surrounding environment, even when no reliable map exists beforehand. The robot combines motion estimates with sensor observations, then uses recognized places or landmarks to limit the drift that accumulates during movement.
SLAM is a problem formulation rather than one product or algorithm. A system may use laser scans, camera images, inertial measurements, or a combination of sensors. The right choice depends on whether you need a 2D or 3D map, what the environment looks like, which sensors and drivers your robot supports, and how much computing and calibration work you can provide.
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What SLAM means in robotics
A mobile robot normally needs two kinds of information:
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- Localization: “Where am I?”
- Mapping: “What is around me?”
SLAM estimates both at the same time. As the robot moves, it tracks its pose—usually position and orientation—and builds a representation of the environment in a shared coordinate frame. That representation can contain obstacles, landmarks, visual features, surfaces, or other information needed for navigation and inspection.
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The two estimates are coupled. A bad pose estimate places new observations in the wrong location, which damages the map. A poor map can then mislead future localization. SLAM algorithms are designed to use the relationship between these estimates rather than treating them as two independent tasks.
The foundational 2016 survey by Cesar Cadena and colleagues describes SLAM as the concurrent construction of an environment model and estimation of the state of the robot moving within it. That definition still captures the central idea, although modern systems use many different mathematical formulations.
SLAM is not the same as navigation
SLAM creates or updates a map and estimates the robot’s global pose. Navigation is the larger system that uses this information to plan paths, avoid obstacles, command the robot’s motors, and recover from blocked routes. A map alone does not make a robot autonomous.
For example, in a typical ROS 2 and Nav2 arrangement, SLAM publishes the map representation and the transform from map to odom. An odometry source supplies the transform from odom to base_link. Nav2’s planners, controllers, costmaps, and behavior components use those transforms along with sensor data.
How a SLAM system works
Although implementations differ, a mobile-robot SLAM pipeline usually follows this cycle:
- Measure motion. Wheel encoders, an IMU, visual odometry, lidar, radar, or sensor fusion provide an estimate of how the robot moved since the previous update.
- Capture observations. A laser scan, camera frame, depth image, or another sensor records features of the environment.
- Estimate the current pose. The system predicts the robot’s new position from motion and then corrects that prediction using the latest observation.
- Insert information into the map. Obstacles, landmarks, surfaces, or visual features are placed in the estimated global frame.
- Match current and previous observations. The system looks for correspondence with known features or places. Correct correspondence is often called data association.
- Detect a loop closure. If the robot recognizes a previously visited area, the new observation creates a constraint linking the current pose to an earlier pose.
- Optimize the estimate. The system distributes accumulated error across poses and map elements so the overall trajectory and map become more globally consistent.
In a simple formulation, the state may contain only position and orientation. More advanced systems can also estimate velocity, inertial-sensor biases, camera-to-body calibration, or other variables. The extra variables can improve accuracy when the sensors and timing are well characterized, but they also increase setup and computational demands.
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Why odometry drifts
Dead reckoning is locally useful but cannot remain perfectly accurate. Wheel slip, uneven floors, encoder quantization, timing errors, and imperfect motion models cause small errors at every update. Those errors accumulate, so a robot that returns to its starting point may appear displaced or rotated relative to its original map.
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Filtering and optimization approaches
The literature includes probabilistic filters, feature-based visual methods, and optimization-based systems such as pose graphs. These approaches differ in how they represent uncertainty, how they incorporate measurements, and how they scale to large environments. There is no single algorithm called “SLAM” that is best for every robot.
Mapping, localization, odometry, and loop closure compared
| Term | Question it answers | Typical role |
|---|---|---|
| Mapping | What does the environment look like? | Builds an occupancy grid, feature map, landmark map, surface model, or another representation. |
| Localization | Where is the robot relative to a map or reference? | Estimates the robot pose using a known map, landmarks, external positioning, or a map being built. |
| Odometry | How did the robot move since the last estimate? | Provides short-term relative motion; it normally drifts over long distances. |
| Loop closure | Have I returned to a previously observed place? | Adds a global constraint that can reduce accumulated trajectory and map error. |
| Navigation | How should the robot reach a goal safely? | Plans paths, follows them, handles obstacles, and coordinates control and recovery. |
A robot does not always need SLAM. If it can localize reliably in a prebuilt map or against known landmarks, localization alone may be sufficient. A factory robot with a prepared map of artificial beacons, or a robot with dependable GPS in an outdoor setting, may not need to construct a map while operating.
Which sensors can provide SLAM?
Lidar is common, but it is not a requirement. Nav2’s state-estimation documentation explicitly notes that a robot does not have to use lidar to use its navigation system. Sensor selection should follow the environment and the map you need rather than a universal rule.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Approach | Primary observations | Good fit | Important trade-offs |
|---|---|---|---|
| 2D laser SLAM | Planar laser range scans, usually combined with odometry | Indoor mobile robots producing a 2D occupancy map | Works well when a clear horizontal scan represents the environment. It requires a correctly configured laser driver, frame IDs, timing, and a compatible LaserScan topic. |
| Visual SLAM | Images from monocular, stereo, or RGB-D cameras | Robots with cameras and environments containing trackable visual features | Performance depends on lighting, texture, motion, camera calibration, and image timing. A monocular camera does not provide scale as directly as stereo or depth sensing. |
| Visual-inertial SLAM | Camera images combined with inertial measurements | Systems that need visual tracking supported by accelerometer and gyroscope data | Camera-IMU calibration and synchronization are essential. Adding an IMU does not automatically correct every visual-tracking failure. |
2D laser systems
A documented ROS 2 route uses SLAM Toolbox with planar laser scans and odometry. The scanner normally publishes sensor_msgs/LaserScan, conventionally on a topic such as /scan, although the topic name can be changed in configuration. The map is usually a 2D occupancy representation suitable for floor-level path planning.
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Camera-based systems
Visual systems extract and match image features or use direct image information. ORB-SLAM3 is a well-known research and software project that supports monocular, stereo, and RGB-D cameras, with pinhole and fisheye camera models. Its visual-inertial modes combine camera observations with inertial measurements.
ORB-SLAM3 is software rather than a sensor. Its repository identifies GPLv3 licensing and says commercial closed-source licensing should be discussed with the authors. Check the license and any dependencies before embedding it in a proprietary product.
Depth, radar, and fused sensors
Depth cameras, radar, lidar, wheel encoders, and IMUs can be used separately or fused. The choice affects range, field of view, performance in darkness or bright light, sensitivity to moving objects, and calibration workload. A sensor that works in a laboratory may be unsuitable for reflective surfaces, dust, repetitive corridors, outdoor weather, or crowded spaces.
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Compare candidate systems against the robot and operating site, not against a generic accuracy claim. The available sources do not establish a universal head-to-head winner among laser, visual, and visual-inertial systems.
- Map dimensionality: Choose 2D if the robot’s decisions occur on a mostly planar floor. Choose a 3D-capable approach when vertical structure, ramps, multiple levels, or full spatial reconstruction matters.
- Environmental appearance: Laser scans can be useful where geometry is clear but visual texture is weak. Cameras need sufficient texture and usable exposure. Repeated corridors, blank walls, glass, reflective surfaces, smoke, and changing illumination can complicate matching.
- Sensor availability: Start with sensors the robot can mount securely and expose through maintained drivers. A theoretically suitable sensor is not useful if its data cannot be synchronized and transformed correctly.
- Calibration burden: Camera intrinsics, camera-to-IMU calibration, sensor extrinsics, and timing offsets matter. Poor calibration can look like an algorithm problem.
- Compute budget: Image processing, dense depth, and large maps can require substantially more processing and memory than a small 2D scan pipeline.
- ROS 2 compatibility: Verify the distribution, driver, message types, frame conventions, and lifecycle behavior supported by the package you intend to run.
- Need for a prior map: If a trusted map and positioning source already exist, localization against that map may be simpler than running SLAM continuously.
ROS 2 example: SLAM Toolbox with Nav2
The following workflow follows the Nav2 Rolling documentation’s SLAM example. Rolling is a moving ROS 2 documentation branch, so package names, launch files, and installation instructions can change. Treat the sequence as an integration plan and verify the exact commands in the documentation for the ROS 2 distribution installed on your robot.
1. Prepare the robot interfaces
Your robot must provide usable motion and sensor data before SLAM can help. At minimum, identify:
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- A base frame, conventionally
base_link. - An odometry frame, conventionally
odom. - One or more sensor frames connected to the base by correct transforms.
- Odometry from wheel encoders, an IMU, visual odometry, lidar, radar, or a fused estimator.
- A laser scan topic carrying
sensor_msgs/LaserScanif you are following the 2D laser path.
Use the actual frame IDs and topic names from your robot. Do not assume that a topic named /scan exists or that a sensor’s frame is already connected to base_link.
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2. Check the transform tree
Nav2 describes the normal relationship as:
map → odom → base_link → sensor frames
During mapping, SLAM supplies the map to odom transform. The odometry system supplies odom to base_link. The sensor driver or robot state publisher supplies the transforms from the base to each sensor.
Confirm that timestamps are advancing, frame IDs are consistent, and sensor extrinsics describe the physical mounting position. A scan with correct ranges but an incorrect frame can be projected into the wrong place and produce a distorted map.
3. Start Nav2 without static-map localization components
The mapping workflow starts Nav2 with its static-map localization components omitted, then launches SLAM. A static-map localization node should not compete with the SLAM node for the global map and transform responsibilities.
The Nav2 tutorial expects robot-specific interfaces to be substituted when using a different platform. Drivers, controller configuration, robot description, sensor topics, and frame names are not interchangeable between robots.
4. Launch SLAM Toolbox
Start SLAM Toolbox with parameters matching the scan topic, frames, update rates, and map mode required by your robot. Verify that the node receives scans and odometry rather than merely checking that the process is running.
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The system should publish a map on /map and provide the map to odom transform. If either is missing, navigation cannot use the mapping result correctly.
5. Inspect the result in RViz
Use RViz to view the map, robot pose, laser data, and transform relationships. Drive slowly through the environment and watch whether walls remain aligned, scans overlay existing geometry, and the robot returns to a previously visited location consistently.
Exploration should include deliberate revisits when practical. A loop closure can only be detected when the system observes enough distinctive information to recognize a previous area.
6. Save the map
When the map covers the required area and the trajectory is consistent, save it with the Nav2 map_saver_cli utility. Record the ROS 2 distribution, SLAM parameters, frame names, sensor configuration, and map resolution alongside the saved files so the map can be reused reliably.
Troubleshooting common SLAM failures
| Symptom | Likely causes to check | Practical response |
|---|---|---|
| No map appears | SLAM is not receiving scans, the topic type is wrong, the frame is missing, or the node is not connected to odometry. | Inspect the scan topic, message type, timestamps, frame IDs, and transform chain before changing algorithm parameters. |
| The map is blank or updates only intermittently | Sensor data is stale, the update rate is unsuitable, the robot is not moving enough, or the scan is outside configured limits. | Check live messages and sensor timestamps, then verify the sensor’s physical field of view and configured ranges. |
| Walls appear doubled or shifted | Incorrect sensor extrinsics, bad odometry, timestamp offsets, or false feature associations. | Recheck the sensor-to-base transform and synchronization. Test with slower, smoother motion and inspect whether errors correlate with turns. |
| The map bends or drifts substantially | Odometry error is accumulating and loop closures are not being detected or are incorrect. | Improve motion estimates and ensure the environment contains repeatable features. Avoid treating an uncertain loop closure as proof that the map is correct. |
| Localization fails in a corridor or blank room | The scene contains too few distinctive visual or geometric features, or many areas look alike. | Consider another sensor modality, add reliable landmarks where appropriate, or use a positioning source suited to the site. |
| The map contains people or moving equipment as fixed obstacles | Dynamic objects were incorporated as if they were part of the permanent environment. | Use obstacle-handling and costmap strategies appropriate to moving objects; do not assume a static SLAM map captures a changing workspace. |
| Processing falls behind | Sensor rates, image resolution, map size, or optimization workload exceed available compute. | Measure CPU, memory, and message latency, then adjust rates or map scope while preserving enough information for reliable matching. |
What SLAM can and cannot guarantee
SLAM can produce a useful map and a globally consistent trajectory, but it is not a push-button guarantee of perfect geometry. Robustness and scalability in long-term mapping remain active challenges identified by the SLAM literature.
Results depend on sensing conditions, motion, calibration, synchronization, data association, compute capacity, and the presence of moving objects. A visually sparse scene can challenge a camera system; repetitive geometry can confuse either visual or geometric matching; reflective or transparent surfaces can produce unreliable measurements; and a map may become stale as the environment changes.
Loop closure improves consistency when the place recognition is correct. A false loop closure can be worse than uncorrected drift because it may warp a large portion of the map. Inspect loop closures and the resulting map instead of assuming every automatic correction is valid.
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Use SLAM when
- The robot must operate without a dependable prior map.
- The environment is indoors or otherwise lacks reliable GPS or external positioning.
- The robot needs to explore while creating a map for later navigation or human inspection.
- You can provide sensors, odometry, calibration, and compute that match the chosen method.
Consider localization without SLAM when
- A validated map already exists and remains representative of the site.
- Known landmarks, beacons, GPS, or another positioning system provide reliable global pose.
- Building a map during operation adds complexity without improving the mission.
- The environment changes so quickly that a persistent map would be misleading.
Implementation checklist
Before calling a mapping run successful, verify each of these items:
- The sensor driver publishes the expected message type and current timestamps.
- Every sensor frame is connected to
base_linkwith correct physical calibration. - Odometry provides a continuous
odomtobase_linktransform. - Only the intended SLAM or localization component publishes the global map relationship.
- The map topic and
maptoodomtransform are visible to Nav2. - RViz shows sensor data aligned with the robot and previously mapped walls.
- The robot revisits recognizable areas and produces sensible loop closures.
- Saved map files are accompanied by the ROS 2 distribution and configuration used to create them.
- The selected method’s license and deployment terms are acceptable for your project.
Bottom line
SLAM lets a robot answer “where am I?” and “what is around me?” together when no trusted map is available. It combines motion estimates with sensor observations, uses loop closures to control drift, and produces information that navigation can consume. A 2D laser with SLAM Toolbox is a documented ROS 2 path, but cameras, depth sensors, inertial measurements, and fused systems are valid alternatives. Choose based on the environment, map requirements, calibration and compute budget, and the reliability of the robot’s interfaces—not on the assumption that one sensor or algorithm is universally best.
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