Simultaneous localization and mapping
While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments.
All quantities are usually probabilistic, so the objective is to compute[1] Applying Bayes' rule gives a framework for sequentially updating the location posteriors, given a map and a transition function
They provide an estimation of the posterior probability distribution for the pose of the robot and for the parameters of the map.
Methods which conservatively approximate the above model using covariance intersection are able to avoid reliance on statistical independence assumptions to reduce algorithmic complexity for large-scale applications.
[2] Other approximation methods achieve improved computational efficiency by using simple bounded-region representations of uncertainty.
Bundle adjustment, and more generally maximum a posteriori estimation (MAP), is another popular technique for SLAM using image data, which jointly estimates poses and landmark positions, increasing map fidelity, and is used in commercialized SLAM systems such as Google's ARCore which replaces their prior augmented reality computing platform named Tango, formerly Project Tango.
New SLAM algorithms remain an active research area,[6] and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed below.
This can include map annotations to the level of marking locations of individual white line segments and curbs on the road.
Essentially such systems simplify the SLAM problem to a simpler localization only task, perhaps allowing for moving objects such as cars and people only to be updated in the map at runtime.
Landmarks are uniquely identifiable objects in the world which location can be estimated by a sensor, such as Wi-Fi access points or radio beacons.
A kind of SLAM for human pedestrians uses a shoe mounted inertial measurement unit as the main sensor and relies on the fact that pedestrians are able to avoid walls to automatically build floor plans of buildings by an indoor positioning system.
[15] For some outdoor applications, the need for SLAM has been almost entirely removed due to high precision differential GPS sensors.
From a SLAM perspective, these may be viewed as location sensors which likelihoods are so sharp that they completely dominate the inference.
However, GPS sensors may occasionally decline or go down entirely, e.g. during times of military conflict, which are of particular interest to some robotics applications.
As a part of the model, the kinematics of the robot is included, to improve estimates of sensing under conditions of inherent and ambient noise.
For 2D robots, the kinematics are usually given by a mixture of rotation and "move forward" commands, which are implemented with additional motor noise.
Unfortunately the distribution formed by independent noise in angular and linear directions is non-Gaussian, but is often approximated by a Gaussian.
Typical loop closure methods apply a second algorithm to compute some type of sensor measure similarity, and reset the location priors when a match is detected.
Active SLAM is generally performed by approximating the entropy of the map under hypothetical actions.
"Multi agent SLAM" extends this problem to the case of multiple robots coordinating themselves to explore optimally.
In neuroscience, the hippocampus appears to be involved in SLAM-like computations,[19][20][21] giving rise to place cells, and has formed the basis for bio-inspired SLAM systems such as RatSLAM.
Collaborative SLAM combines sensors from multiple robots or users to generate 3D maps.
An observer, or robot must be equipped with a microphone array to enable use of Acoustic SLAM, so that DoA features are properly estimated.
Originally designed for human–robot interaction, Audio-Visual SLAM is a framework that provides the fusion of landmark features obtained from both the acoustic and visual modalities within an environment.
drones, service robots), it is valuable to use low-power, lightweight equipment such as monocular cameras, or microelectronic microphone arrays.
The susceptibility of audio sensors to reverberation, sound source inactivity, and noise can also be accordingly compensated through fusion of landmark beliefs from the visual modality.
A seminal work in SLAM is the research of Smith and Cheeseman on the representation and estimation of spatial uncertainty in 1986.
[28][29] Other pioneering work in this field was conducted by the research group of Hugh F. Durrant-Whyte in the early 1990s.
The acronym SLAM was coined within the paper, "Localization of Autonomous Guided Vehicles" which first appeared in ISR in 1995.
Mass-market SLAM implementations can now be found in consumer robot vacuum cleaners[32] and virtual reality headsets such as the Meta Quest 2 and PICO 4 for markerless inside-out tracking.
