Hardware & control
SLAM
Simultaneous localisation and mapping
SLAM is the joint estimation of a moving robot or sensor rig's pose and a map of its environment from sensor observations. It addresses the coupled problem of needing a map to localise while needing pose estimates to build that map.
Also known as: simultaneous localization and mapping
Updated
Estimating pose and a map together
In SLAM, new sensor observations help estimate both where the sensor is and what its surroundings are like. The map is not just a visualisation attached to a trajectory: it provides a reference that can be used again for localisation. The ORB-SLAM3 paper makes this map-reuse objective explicit in its discussion of visual SLAM and odometry.
SLAM is a problem class, not the name of one sensor or algorithm. ORB-SLAM3 illustrates several camera-based configurations, including monocular, stereo, RGB-D and visual-inertial operation. Its particular representations and sensor support should not be treated as mandatory features of every SLAM system.
Revisited places can constrain accumulated drift
A loop closure associates a current observation with an earlier mapped place. That relationship can support correction of accumulated trajectory and map error. ORB-SLAM3 describes place recognition, geometric consistency checks and subsequent optimisation, as well as merging maps from different sessions.
Revisiting a place is not an automatic accuracy guarantee. The system must establish the correct correspondence; weak visual information or an incorrect match can undermine estimation. ORB-SLAM3 identifies low-texture environments as a failure case for its feature-based approach. Its benchmark results do not establish the accuracy of an unrelated robot, camera or environment.
Visual-inertial odometry focuses on motion estimation and may discard older scene information. Some odometry systems add loop closing or mapping, so the practical distinction depends on what history they retain and reuse, not merely the software's label.
A SLAM map is not automatically a task-ready digital scene
A map sufficient for localisation may not contain the dense geometry, object identities, physical properties or contact surfaces needed for simulation and manipulation. Buyers of humanoid or egocentric datasets should ask what the exported map actually contains, rather than assuming that a file called a map is a complete reconstruction.
For trajectory use, record the coordinate frame, scale convention, calibration, mapping version and whether estimates were produced online or revised after loop closure. Preserve reset boundaries and map identifiers when tracking restarts. A retrospectively optimised trajectory can be useful supervision, but it must not be confused with the pose estimate a deployed controller could access at that instant.
Sources
Related terms
Data & collection
Visual-inertial odometry
Visual-inertial odometry estimates a moving sensor rig's motion by combining camera observations with inertial measurements from accelerometers and gyroscopes. Its core output is a relative motion or pose estimate over time, not an independently measured ground-truth trajectory or necessarily a reusable global map.
Models & learning
Pose estimation
Pose estimation is the process of inferring the position and orientation of a body, object, camera, hand, or robot relative to a specified coordinate frame. In three-dimensional robotics this is often called 6D or 6-DoF pose estimation because the result has three translational and three rotational degrees of freedom, even when orientation is stored with more than three numbers.
Hardware & control
State estimation
State estimation is the process of inferring variables that are not known exactly—such as a robot’s base pose, velocity, contact state or sensor bias—from noisy measurements, control inputs and a model of how the system evolves. An estimator should provide both an estimate and, where possible, a representation of its uncertainty.
Hardware & control
Coordinate frame
A coordinate frame is a defined origin and set of oriented axes used to express positions, orientations, motions, forces, or other spatial quantities. A value has no complete geometric meaning until its frame and convention are known. Transformations relate measurements expressed in frames such as world, robot base, camera, end effector, object, or sensor.
Data & collection
Depth data
Depth data records the distance associated with image locations or sensor rays, usually as a depth image in which each pixel stores a metric value relative to a camera. The exact geometry, units, invalid-value convention, and coordinate frame depend on the sensor and encoding. RGB-D data pairs depth with colour imagery; a point cloud is a separate 3D representation derived from or aligned with such measurements.