Simultaneous localization and mapping

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2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. Stanley2.JPG
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system.
A map generated by a SLAM Robot RoboCup Rescue arena map generated by robot Hector from Darmstadt at 2010 German open.jpg
A map generated by a SLAM Robot

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. 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. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.

Contents

SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance. Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body.

Mathematical description of the problem

Given a series of controls and sensor observations over discrete time steps , the SLAM problem is to compute an estimate of the agent's state and a map of the environment . 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 ,

Similarly the map can be updated sequentially by

Like many inference problems, the solutions to inferring the two variables together can be found, to a local optimum solution, by alternating updates of the two beliefs in a form of an expectation–maximization algorithm.

Algorithms

Statistical techniques used to approximate the above equations include Kalman filters and particle filters (the algorithm behind Monte Carlo Localization). 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. [3]

Set-membership techniques are mainly based on interval constraint propagation. [4] [5] They provide a set which encloses the pose of the robot and a set approximation of the map. 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. MAP estimators compute the most likely explanation of the robot poses and the map given the sensor data, rather than trying to estimate the entire posterior probability.

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. Many SLAM systems can be viewed as combinations of choices from each of these aspects.

Mapping

Topological maps are a method of environment representation which capture the connectivity (i.e., topology) of the environment rather than creating a geometrically accurate map. Topological SLAM approaches have been used to enforce global consistency in metric SLAM algorithms. [7]

In contrast, grid maps use arrays (typically square or hexagonal) of discretized cells to represent a topological world, and make inferences about which cells are occupied. Typically the cells are assumed to be statistically independent to simplify computation. Under such assumption, are set to 1 if the new map's cells are consistent with the observation at location and 0 if inconsistent.

Modern self driving cars mostly simplify the mapping problem to almost nothing, by making extensive use of highly detailed map data collected in advance. This can include map annotations to the level of marking locations of individual white line segments and curbs on the road. Location-tagged visual data such as Google's StreetView may also be used as part of maps. 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.

Sensing

Accumulated registered point cloud from lidar SLAM Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco.png
Accumulated registered point cloud from lidar SLAM

SLAM will always use several different types of sensors, and the powers and limits of various sensor types have been a major driver of new algorithms. [8] Statistical independence is the mandatory requirement to cope with metric bias and with noise in measurements. Different types of sensors give rise to different SLAM algorithms which assumptions are most appropriate to the sensors. At one extreme, laser scans or visual features provide details of many points within an area, sometimes rendering SLAM inference unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration. At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM. Most practical SLAM tasks fall somewhere between these visual and tactile extremes.

Sensor models divide broadly into landmark-based and raw-data approaches. 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. Raw-data approaches make no assumption that landmarks can be identified, and instead model directly as a function of the location.

Optical sensors may be one-dimensional (single beam) or 2D- (sweeping) laser rangefinders, 3D high definition light detection and ranging (lidar), 3D flash lidar, 2D or 3D sonar sensors, and one or more 2D cameras. [8] Since the invention of local features, such as SIFT, there has been intense research into visual SLAM (VSLAM) using primarily visual (camera) sensors, because of the increasing ubiquity of cameras such as those in mobile devices. [9] Follow up research includes. [10] Both visual and lidar sensors are informative enough to allow for landmark extraction in many cases. Other recent forms of SLAM include tactile SLAM [11] (sensing by local touch only), radar SLAM, [12] acoustic SLAM, [13] and Wi-Fi-SLAM (sensing by strengths of nearby Wi-Fi access points). [14] Recent approaches apply quasi-optical wireless ranging for multi-lateration (real-time locating system (RTLS)) or multi-angulation in conjunction with SLAM as a tribute to erratic wireless measures. 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.

Kinematics modeling

The term represents the kinematics of the model, which usually include information about action commands given to a robot. 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. The dynamic model balances the contributions from various sensors, various partial error models and finally comprises in a sharp virtual depiction as a map with the location and heading of the robot as some cloud of probability. Mapping is the final depicting of such model, the map is either such depiction or the abstract term for the model.

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. An alternative approach is to ignore the kinematic term and read odometry data from robot wheels after each command—such data may then be treated as one of the sensors rather than as kinematics.

Moving objects

Non-static environments, such as those containing other vehicles or pedestrians, continue to present research challenges. [16] [17] SLAM with DATMO is a model which tracks moving objects in a similar way to the agent itself. [18]

Loop closure

Loop closure is the problem of recognizing a previously-visited location and updating beliefs accordingly. This can be a problem because model or algorithm errors can assign low priors to the location. 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. For example, this can be done by storing and comparing bag of words vectors of scale-invariant feature transform (SIFT) features from each previously visited location.

Exploration

Active SLAM studies the combined problem of SLAM with deciding where to move next to build the map as efficiently as possible. The need for active exploration is especially pronounced in sparse sensing regimes such as tactile SLAM. 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.

Biological inspiration

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

Collaborative SLAM combines sensors from multiple robots or users to generate 3D maps. [22] This capability was demonstrated by a number of teams in the 2021 DARPA Subterranean Challenge.

Specialized SLAM methods

Acoustic SLAM

An extension of the common SLAM problem has been applied to the acoustic domain, where environments are represented by the three-dimensional (3D) position of sound sources, termed aSLAM (Acoustic Simultaneous Localization and Mapping). [23] Early implementations of this technique have used direction-of-arrival (DoA) estimates of the sound source location, and rely on principal techniques of sound localization to determine source locations. An observer, or robot must be equipped with a microphone array to enable use of Acoustic SLAM, so that DoA features are properly estimated. Acoustic SLAM has paved foundations for further studies in acoustic scene mapping, and can play an important role in human-robot interaction through speech. To map multiple, and occasionally intermittent sound sources, an acoustic SLAM system uses foundations in random finite set theory to handle the varying presence of acoustic landmarks. [24] However, the nature of acoustically derived features leaves Acoustic SLAM susceptible to problems of reverberation, inactivity, and noise within an environment.

Audiovisual SLAM

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. [25] Human interaction is characterized by features perceived in not only the visual modality, but the acoustic modality as well; as such, SLAM algorithms for human-centered robots and machines must account for both sets of features. An Audio-Visual framework estimates and maps positions of human landmarks through use of visual features like human pose, and audio features like human speech, and fuses the beliefs for a more robust map of the environment. For applications in mobile robotics (ex. drones, service robots), it is valuable to use low-power, lightweight equipment such as monocular cameras, or microelectronic microphone arrays. Audio-Visual SLAM can also allow for complimentary function of such sensors, by compensating the narrow field-of-view, feature occlusions, and optical degradations common to lightweight visual sensors with the full field-of-view, and unobstructed feature representations inherent to audio sensors. 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. Complimentary function between the audio and visual modalities in an environment can prove valuable for the creation of robotics and machines that fully interact with human speech and human movement.

Implementation methods

Various SLAM algorithms are implemented in the open-source software Robot Operating System (ROS) libraries, often used together with the Point Cloud Library for 3D maps or visual features from OpenCV.

EKF SLAM

In robotics, EKF SLAM is a class of algorithms which uses the extended Kalman filter (EKF) for SLAM. Typically, EKF SLAM algorithms are feature based, and use the maximum likelihood algorithm for data association. In the 1990s and 2000s, EKF SLAM had been the de facto method for SLAM, until the introduction of FastSLAM. [26]

Associated with the EKF is the gaussian noise assumption, which significantly impairs EKF SLAM's ability to deal with uncertainty. With greater amount of uncertainty in the posterior, the linearization in the EKF fails. [27]

GraphSLAM

In robotics, GraphSLAM is a SLAM algorithm which uses sparse information matrices produced by generating a factor graph of observation interdependencies (two observations are related if they contain data about the same landmark). [27] It is based on optimization algorithms.

History

A seminal work in SLAM is the research of R.C. Smith and P. 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. [30] which showed that solutions to SLAM exist in the infinite data limit. This finding motivates the search for algorithms which are computationally tractable and approximate the solution. The acronym SLAM was coined within the paper, "Localization of Autonomous Guided Vehicles" which first appeared in ISR in 1995. [31]

The self-driving STANLEY and JUNIOR cars, led by Sebastian Thrun, won the DARPA Grand Challenge and came second in the DARPA Urban Challenge in the 2000s, and included SLAM systems, bringing SLAM to worldwide attention. 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.

See also

Related Research Articles

Robotic mapping is a discipline related to computer vision and cartography. The goal for an autonomous robot is to be able to construct a map or floor plan and to localize itself and its recharging bases or beacons in it. Robotic mapping is that branch which deals with the study and application of ability to localize itself in a map / plan and sometimes to construct the map or floor plan by the autonomous robot.

The condensation algorithm is a computer vision algorithm. The principal application is to detect and track the contour of objects moving in a cluttered environment. Object tracking is one of the more basic and difficult aspects of computer vision and is generally a prerequisite to object recognition. Being able to identify which pixels in an image make up the contour of an object is a non-trivial problem. Condensation is a probabilistic algorithm that attempts to solve this problem.

<span class="mw-page-title-main">Transfer learning</span> Machine learning technique

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing/transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency.

Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment. The algorithm uses a particle filter to represent the distribution of likely states, with each particle representing a possible state, i.e., a hypothesis of where the robot is. The algorithm typically starts with a uniform random distribution of particles over the configuration space, meaning the robot has no information about where it is and assumes it is equally likely to be at any point in space. Whenever the robot moves, it shifts the particles to predict its new state after the movement. Whenever the robot senses something, the particles are resampled based on recursive Bayesian estimation, i.e., how well the actual sensed data correlate with the predicted state. Ultimately, the particles should converge towards the actual position of the robot.

<span class="mw-page-title-main">Wolfram Burgard</span> German roboticist

Wolfram Burgard is a German roboticist. He is a full professor at the University of Technology Nuremberg where he heads the Laboratory for Robotics and Artificial Intelligence. He is known for his substantial contributions to the simultaneous localization and mapping (SLAM) problem as well as diverse other contributions to robotics.

<span class="mw-page-title-main">Indoor positioning system</span> Network of devices used to wirelessly locate objects inside a building

An indoor positioning system (IPS) is a network of devices used to locate people or objects where GPS and other satellite technologies lack precision or fail entirely, such as inside multistory buildings, airports, alleys, parking garages, and underground locations.

<span class="mw-page-title-main">Robot navigation</span> Robots ability to navigate

Robot localization denotes the robot's ability to establish its own position and orientation within the frame of reference. Path planning is effectively an extension of localization, in that it requires the determination of the robot's current position and a position of a goal location, both within the same frame of reference or coordinates. Map building can be in the shape of a metric map or any notation describing locations in the robot frame of reference.

Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known. Occupancy grids were first proposed by H. Moravec and A. Elfes in 1985.

<span class="mw-page-title-main">3D reconstruction</span> Process of capturing the shape and appearance of real objects

In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction.

<span class="mw-page-title-main">Visual odometry</span> Determining the position and orientation of a robot by analyzing associated camera images

In robotics and computer vision, visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of robotic applications, such as on the Mars Exploration Rovers.

In robotics, the exploration problem deals with the use of a robot to maximize the knowledge over a particular area. The exploration problem arises in robotic mapping and search & rescue situations, where an environment might be dangerous or inaccessible to humans.

<span class="mw-page-title-main">Mobile Robot Programming Toolkit</span>

The Mobile Robot Programming Toolkit (MRPT) is a cross-platform and open source C++ library aimed to help robotics researchers to design and implement algorithms related to Simultaneous Localization and Mapping (SLAM), computer vision and motion planning. Different research groups have employed MRPT to implement projects reported in some of the major robotics journals and conferences.

Covariance intersection (CI) is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown.

<span class="mw-page-title-main">Velocity obstacle</span> Term in robotics and motion planning

In robotics and motion planning, a velocity obstacle, commonly abbreviated VO, is the set of all velocities of a robot that will result in a collision with another robot at some moment in time, assuming that the other robot maintains its current velocity. If the robot chooses a velocity inside the velocity obstacle then the two robots will eventually collide, if it chooses a velocity outside the velocity obstacle, such a collision is guaranteed not to occur.

The Robotics Toolbox is MATLAB toolbox software that supports research and teaching into arm-type and mobile robotics. While the Robotics Toolbox is free software, it requires the proprietary MATLAB environment in order to execute. The Toolbox forms the basis of the exercises in several textbooks.

3D sound localization refers to an acoustic technology that is used to locate the source of a sound in a three-dimensional space. The source location is usually determined by the direction of the incoming sound waves and the distance between the source and sensors. It involves the structure arrangement design of the sensors and signal processing techniques.

<span class="mw-page-title-main">Pose tracking</span>

In virtual reality (VR) and augmented reality (AR), a pose tracking system detects the precise pose of head-mounted displays, controllers, other objects or body parts within Euclidean space. Pose tracking is often referred to as 6DOF tracking, for the six degrees of freedom in which the pose is often tracked.

<span class="mw-page-title-main">Margarita Chli</span> Greek computer vision and robotics researcher

Margarita Chli is an assistant professor and leader of the Vision for Robotics Lab at ETH Zürich in Switzerland. Chli is a leader in the field of computer vision and robotics and was on the team of researchers to develop the first fully autonomous helicopter with onboard localization and mapping. Chli is also the Vice Director of the Institute of Robotics and Intelligent Systems and an Honorary Fellow of the University of Edinburgh in the United Kingdom. Her research currently focuses on developing visual perception and intelligence in flying autonomous robotic systems.

<span class="mw-page-title-main">Layered costmaps</span> Delay-based audio effects system

Layered costmaps is a method to create and update maps for robot navigation and path planning proposed by David V. Lu in 2014. During robot navigation, layered costmaps can abstract the realistic environment around the robot into maps that can be comprehended by robot navigation methods. The method consists of more than one layer of costmaps, each of which describes obstacles with different properties. Each layered costmap consists of grids and is represented as a matrix, the values of the matrix elements are related to the risks of grids. In the layered costmap, the colour of a grid indicates the risk of moving through that grid. There is a high probability of robot collision when the robot moves through a grid with deep colour. When updating a layered costmap, only the area covered by the sensors in the map is updated, rather than the entire map.

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