The Joy of SLAM

The Joy of SLAMSamantha Ahern - @2standandstareCentre for Computational Intelligence, De Montfort University

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SLAM Presentation PlanSimultaneous Localisation and Mapping (SLAM) is the core element of navigation systems for mobile robots and vehicles. In this talk I will discuss how SLAM works, the main implementation methods and examples of their applications.

I will discuss my own work in implementing a SLAM system on a small autonomous robot and discuss the parallels with autonomous vehicles.

Wheres Johnny?An autonomous agent needs to know:About its environment Pre-existing mapCreates a map as it exploresWhere it is in relation to its environment

Types of SLAMFeature-based SLAMPose-based SLAMAppearance-based SLAMVariants - these include Active SLAM and Multi-robot SLAM.

E. Zamora and W. Yu, Recent advances on simultaneous localization and mapping for mobile robots, IETE Tech. Rev. Inst. Electron. Telecommun. Eng. India, vol. 30, no. 6, pp. 490496, 2013.

Feature-based SLAM: It is the most popular approach to solve the SLAM problem. It uses predefined landmarks and environment model to estimate the robot current state (or robot path) and the map [1].

Pose-Based: Only the robot state trajectory is estimated, without landmark positions. The robot path is estimated using constraints imposed by the landmark positions or the raw laser (or visual) data.

Appearance-based:It does not use metric information and the landmark positions. The robot path is not tracked in metric sense. The visual images or spatial information are utilized to recognize the place. It is very common that these appearance techniques are used complementary to any metric SLAM method to detect loop closures [7].

Active SLAM derives a control law for robot navigation in order to achieve efficiently a certain desired accuracy of the robot location and the map [10]. Multi-robot SLAM uses many robots for large environment [11]. 4

Localisation techniquesHistogram FilterKalman Filter / Extended Kalman FilterParticle Filter

Belief: ProbabilitySense: Product followed by normalizationMove: Convolution (addition)

Histogram Filter:Discrete state estimation very rarely used

Kalman Filter / Extended Kalman filter:Used in feature-based slam

Particle filter:Used in filter and posed based SLAM5

Histogram Filters

Histogram Filters contd

Kalman FiltersEstimates continuous statesUni-modal distribution (Gaussian)

KF: Update on measurement

KF: Update on motion

Multivariate gaussians can be used to infer velocity from measurement update10

Kalman Filter UpdatesX = estimateP = uncertainty covarianceF = state transition matrixU = motion vectorZ = measurementH = measurement functionR = measurement noiseI = identity matrix

Particle FiltersEasiest to program of the 3 filtersEstimates continuous statesHas a multi-modal distributionAll calculations are approximateLevel of efficiency is unclear

PF: Core conceptsParticles consist of:x positiony positionDirectionN is the number of particles

Possible position of robot (particle)For each particle (.) the expected distance from each point (.) is calculated.

Mismatch between expected and actual measurements determine weight.

Higher weighted particles are more likely to survive resampling.

PF: Re-sampling - weights

N

PF: Re-sampling - replacementsHigher weighted particles are more likely to survive resampling

Draw withreplacements

PF: Re-sampling wheel

PF: Equations Particles Importance weights Re-sampling Samples

Sample

MappingOccupancy GridsTopological graphsFeature Map ( vision data )

http://www.cs.cmu.edu/~motionplanning/papers/sbp_papers/integrated4/elfes_occup_grids.pdfhttps://www.udacity.com/course/viewer#!/c-cs373/l-48696626/m-48701349

Occupancy GridsOccupancy grids utilise random field representation, Each cell in the grid stores a probabilistic estimate of the cell's state. The probabilistic estimate is obtained through the integration and interpretation of sensor data from multiple sensors of the same type or different complimentary sensor types. Occupancy grids can incorporate positional uncertainty into the mapping process.

http://www.cs.cmu.edu/~motionplanning/papers/sbp_papers/integrated4/elfes_occup_grids.pdf

Open RatSLAM Inspired by the rodent hippocampal complexHybrid method combining characteristics of:Feature basedGrid based Topological SLAM techniques.Consists of four nodes: Pose Cell Network Local View CellsExperience Map Visual Odometry (for image only datasets).Developed by Queensland University of Technology

Open RatSLAM

http://link.springer.com.libproxy.ucl.ac.uk/article/10.1007/s10514-012-9317-9/fulltext.html

RatSLAM video

For my dissertation project am implementing a version in NXC using sonar sensors, translation from Robot C22

DELPHI Drive

http://www.delphi.com/delphi-drive

Uses detailed map for driving in urban areas but on highways builds grids23

DELPHI Car technologyThe Delphi car is fitted with:Radar:Long Range Radar x 6360Dg Radar x 44 Layer LiDAR x 6Cameras:Forward cameraHD cameraInfra-red cameraPlus:GPS AntennaeWheel odometers

Where am I? What do I need to see?

Where am I? -> LocalisationWhat do I need to see? -> Vision, perception and mapping

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Semi - Autonomous Vehicles: Platoons

http://www.sartre-project.eu/en/Sidor/default.aspx

The project aims to encourage a step change in personal transport usage by developing of environmental roadtrainscalled platoons.Systems will be developed facilitating the safe adoption of road trains on un-modified public highways with interaction with other traffic. A scheme will be developed whereby a lead vehicle with a professional driver will take responsibility for a platoon. Following vehicles will enter a semi-autonomous control mode that allows the driver of the following vehicle todo other thingsthat would normally be prohibited for reasons of safety; for example, operate a phone, reading a book or watching a movie.

Other research projects are working on fully autonomous version the first vehicle implements full SLAM, following vehicles localisation and comms between vehicles?25

Autonomous vehicles main difficultiesNoisy dataIncompletenessDynamicityDiscrete measurements in real-timeKey blocks

Will / can it do the right thing? Hybrid agent architectureControl SystemWhat it doesRational AgentWhy it does it

Control SystemLow LevelRational AgentHigh LevelAutonomous System

Who is in control? When should control be handed back? Should return to human control be refused?Ethics? System can order options based on ethical priorities Save humans >> save animals >> save property27

Verifying the Rational AgentProbabilisticDeterminiteInfiniteNon-determinateFinite

Finite Abstraction

EI: Sensors / actuatorsFC: Control system etc.DM: Rational agent28

Essential elements - A S/A VehiclesSensors and perceptionComputing platforms & control systemsElectrical architecture & network managementVehicle connectivityUser experienceOff-board (cloud) support & servicesFunctional safety & cyber security

From DELPHI29

Conclusions

93% road accidents caused by human errorPerception and decision making take place under uncertaintyBayesian estimators are used for localisation and mappingInteraction between driver and autonomous / semi-autonomous vehicle needs to be managedInteraction between autonomous, semi-autonomous and manual vehicles needs to be managedSame concepts used by autonomous drones

Referenceshttps://www.udacity.com/course/progress#!/c-cs373http://www.sartre-project.eu/en/Sidor/default.aspxhttp://www.delphi.com/delphi-driveJ. Borenstein, H. R. Everett, L. Feng, and D. Wehe, Mobile robot positioning: Sensors and techniques, J. Robot. Syst., vol. 14, no. 4, pp. 231249, 1997.A. Elfes, Using occupancy grids for mobile robot perception and navigation, Computer, vol. 22, no. 6, pp. 4657, Jun. 1989.E. Zamora and W. Yu, Recent advances on simultaneous localization and mapping for mobile robots, IETE Tech. Rev. Inst. Electron. Telecommun. Eng. India, vol. 30, no. 6, pp. 490496, 2013.D. Ball, S. Heath, J. Wiles, G. Wyeth, P. Corke, and M. Milford, OpenRatSLAM: an open source brain-based SLAM system, Auton. Robots, vol. 34, no. 3, pp. 149176, 2013.R. Smith, M. Self, and P. Cheeseman, Estimating uncertain spatial relationships in robotics, in 1987 IEEE International Conference on Robotics and Automation. Proceedings, 1987, vol. 4, pp. 850850.

Questions