Sebastian Thrun Carnegie Mellon & Stanford Wolfram Burgard University of Freiburg and Dieter Fox University of Washington Probabilistic Algorithms for Mobile Robot Mapping LEP: Adapted, combining partially with Thrun’s Tutorial

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Based on the paper A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping Best paper award at 2000 IEEE International Conference on Robotics and Automation (~1,100 submissions) Sponsored by DARPA (TMR-J.Blitch, MARS-D.Gage, MICA-S.Heise) and NSF (ITR(2), CAREER-E.Glinert, IIS-V.Lumelsky) Other contributors: Yufeng Liu, Rosemary Emery, Deepayan Charkrabarti, Frank Dellaert, Michael Montemerlo, Reid Simmons, Hugh Durrant-Whyte, Somajyoti Majnuder, Nick Roy, Joelle Pineau, …

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Open Problems 3D Mapping with EM Real Time Hybrid Expectation Maximization SLAM (Kalman filters) Motivation

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Museum Tour-Guide Robots With: Greg Armstrong, Michael Beetz, Maren Benewitz, Wolfram Burgard, Armin Cremers, Frank Dellaert, Dieter Fox, Dirk Haenel, Chuck Rosenberg, Nicholas Roy, Jamie Schulte, Dirk Schulz

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Nursebot Initiative With: Greg Armstrong, Greg Baltus, Jacqueline Dunbar- Jacob, Jennifer Goetz, Sara Kiesler, Judith Matthews, Colleen McCarthy, Michael Montemerlo, Joelle Pineau, Martha Pollack, Nicholas Roy, Jamie Schulte

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Localization Problem  Estimate robot’s coordinates s=(x,y,  ) from sensor data Position tracking (error bounded) Global localization (unbounded error) Kidnapping (recovery from failure) Ingemar Cox (1991): “Using sensory information to locate the robot in its environment is the most fundamental problem to provide a mobile robot with autonomous capabilities.” see also [Borenstein et al, 96]

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping: The Problem n Concurrent Mapping and Localization (CML) n Simultaneous Localization and Mapping (SLAM)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping: The Problem n Continuous variables n High-dimensional (eg, 1,000,000+ dimensions) n Multiple sources of noise n Simulation not acceptable

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Milestone Approaches Mataric 1990 Kuipers et al 1991 Elfes/Moravec 1986 Lu/Milios/Gutmann 1997

Sebastian Thrun, Carnegie Mellon, IJCAI D Mapping Konolige et al, 2001Teller et al, 2000 Moravec et al, 2000

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Take-Home Message Mapping is the holy grail in mobile robotics. Every state-of-the-art mapping algorithm is probabilistic.

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Robots are Inherently Uncertain n Uncertainty arises from four major factors: –Environment stochastic, unpredictable –Robot stochastic –Sensor limited, noisy –Models inaccurate

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Probabilistic Robotics

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Probabilistic Robotics Key idea: Explicit representation of uncertainty (using the calculus of probability theory) n Perception = state estimation n Action = utility optimization

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Advantages of Probabilistic Paradigm n Can accommodate inaccurate models n Can accommodate imperfect sensors n Robust in real-world applications n Best known approach to many hard robotics problems

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Pitfalls n Computationally demanding n False assumptions n Approximate

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Open Problems 3D Mapping with EM Real Time Hybrid Expectation Maximization Motivation SLAM (Kalman filters)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Localization Problem  Estimate robot’s coordinates s=(x,y,  ) from sensor data Position tracking (error bounded) Global localization (unbounded error) Kidnapping (recovery from failure) Ingemar Cox (1991): “Using sensory information to locate the robot in its environment is the most fundamental problem to provide a mobile robot with autonomous capabilities.” see also [Borenstein et al, 96]

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 s p(s)p(s) Probabilistic Localization [Simmons/Koenig 95] [Kaelbling et al 96] [Burgard et al 96]

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Bayes Filters Bayes Markov [Kalman 60, Rabiner 85] d = data o = observation a = action t = time s = state Markov

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Markov Assumption used above Knowledge of current state renders past, future independent: “Static World Assumption” “Independent Noise Assumption”

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Bayes Filters are Familiar to AI! n Kalman filters n Hidden Markov Models n Dynamic Bayes networks n Partially Observable Markov Decision Processes (POMDPs)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Localization With Bayes Filters map m s’ a p(s|a,s’,m) a s’ laser datap(o|s,m) observation o

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Kalman filter [Schiele et al. 94], [Weiß et al. 94], [Borenstein 96], [Gutmann et al. 96, 98], [Arras 98] Piecewise constant (metric, topological) [Nourbakhsh et al. 95], [Simmons et al. 95], [Kaelbling et al. 96], [Burgard et al. 96], [Konolige et al. 99] Variable resolution (eg, trees) [Burgard et al. 98] Multi-hypothesis [Weckesser et al. 98], [Jensfelt et al. 99] What is the Right Representation?

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Idea: Represent Belief Through Samples Particle filters [Doucet 98, deFreitas 98] Condensation algorithm [Isard/Blake 98] Monte Carlo localization [Fox/Dellaert/Burgard/Thrun 99]

Monte Carlo Localization (MCL)

MCL: Importance Sampling

MCL: Robot Motion motion

MCL: Importance Sampling

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Particle Filters draw s (i) t  1 from b ( s t  1 ) draw s (i) t from p ( s t | s (i) t  1,a t  1,m ) Represents b ( s t ) by set of weighted particles {s (i) t,w (i) t } Importance factor for s (i) t :

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Monte Carlo Localization

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Performance Comparison Monte Carlo localizationMarkov localization (grids)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Monte Carlo Localization n Approximate Bayes Estimation/Filtering –Full posterior estimation –Converges in O(1/  #samples) [Tanner’93] –Robust: multiple hypothesis with degree of belief –Efficient: focuses computation where needed –Any-time: by varying number of samples –Easy to implement

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Pitfall: The World is not Markov! [Fox et al 1998] Distance filters:

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Probabilistic Localization: Lessons Learned n Probabilistic Localization = Bayes filters n Particle filters: Approximate posterior by random samples

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Problem: Concurrent Mapping and Localization 70 m

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Concurrent Mapping and Localization n Is a chicken-and-egg problem –Mapping with known poses is “simple” –Localization with known map is “simple” –But in combination, the problem is hard! n Today’s best solutions are all probabilistic!

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Posterior estimation with known poses: Occupancy grids Posterior estimation with known poses: Occupancy grids Maximum likelihood: ML* Maximum likelihood: ML* Maximum likelihood: EM Maximum likelihood: EM Posterior estimation: EKF (SLAM) Posterior estimation: EKF (SLAM) Mapping: Outline

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping as Posterior Estimation Assume static map [Smith, Self, Cheeseman 90, Chatila et al 91, Durrant-Whyte et al 92-00, Leonard et al ]

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Kalman Filters n N-dimensional Gaussian n Can handle hundreds of dimensions

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Underwater Mapping By: Louis L. Whitcomb, Johns Hopkins University

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Underwater Mapping - Example “Autonomous Underwater Vehicle Navigation,” John Leonard et al, 1998

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Underwater Mapping with SLAM Courtesy of Hugh Durrant-Whyte, Univ of Sydney

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping with Extended Kalman Filters Courtesy of [Leonard et al 1998]

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Key Assumption Inverse sensor model p(s t |o t,m) must be Gaussian. n Main problem: Data association Posterior multi-modal  Undistinguishable features  In practice: Extract small set of highly distinguishable features from sensor data Discard all other data If ambiguous, take best guess for landmark identity Posterior uni-modal Distinguishable features

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping Algorithms - Comparison SLAM (Kalman) OutputPosterior ConvergenceStrong Local minimaNo Real timeYes Odom. ErrorUnbounded Sensor NoiseGaussian # Features10 3 Feature uniqYes Raw dataNo

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Posterior estimation with known poses: Occupancy grids Posterior estimation with known poses: Occupancy grids Maximum likelihood: ML* Maximum likelihood: ML* Maximum likelihood: EM Maximum likelihood: EM Posterior estimation: EKF (SLAM) Posterior estimation: EKF (SLAM) Mapping: Outline

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 M-Step: Mapping with known posesE-Step: Localization [Dempster et al, 77] [Thrun et al, 1998] [Shatkay/Kaelbling 1997] Mapping with Expectation Maximization

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 map(1) Uncertainty Models for Motion

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 CMU’s Wean Hall (80 x 25 meters) 15 landmarks 16 landmarks 17 landmarks27 landmarks

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 EM Mapping, Example (width 45 m)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping Algorithms - Comparison SLAM (Kalman) EM OutputPosteriorML/MAP ConvergenceStrongWeak? Local minimaNoYes Real timeYesNo Odom. ErrorUnbounded Sensor NoiseGaussianAny # Features10 3  Feature uniqYesNo Raw dataNoYes

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Posterior estimation with known poses: Occupancy grids Posterior estimation with known poses: Occupancy grids Maximum likelihood: ML* Maximum likelihood: ML* Maximum likelihood: EM Maximum likelihood: EM Posterior estimation: EKF (SLAM) Posterior estimation: EKF (SLAM) Mapping: Outline

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Goal EM: data association Not real-time Kalman filters: real-time No data association ? ?

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Real-Time Approximation (ICRA paper)   Incremental ML

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Incremental ML: Not A Good Idea path robot mismatch

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 ML* Mapping, Online Idea: step-wise maximum likelihood 2. Posterior: [Gutmann/Konolige 00, Thrun et al. 00] 1. Incremental ML estimate:

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping with Poor Odometry map and exploration path raw data DARPA Urban Robot

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping Without(!) Odometry mapraw data (no odometry)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Localization in Multi-Robot Mapping

Sebastian Thrun, Carnegie Mellon, IJCAI D Mapping two laser range finders

Sebastian Thrun, Carnegie Mellon, IJCAI D Structure Mapping (Real-Time)

Sebastian Thrun, Carnegie Mellon, IJCAI D Texture Mapping raw image sequencepanoramic camera

Sebastian Thrun, Carnegie Mellon, IJCAI D Texture Mapping

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Underwater Mapping (with University of Sydney) With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping Algorithms - Comparison SLAM (Kalman) EMML* OutputPosteriorML/MAP ConvergenceStrongWeak?No Local minimaNoYes Real timeYesNoYes Odom. ErrorUnbounded Sensor NoiseGaussianAny # Features10 3  Feature uniqYesNo Raw dataNoYes

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Posterior estimation with known poses: Occupancy grids Posterior estimation with known poses: Occupancy grids Maximum likelihood: ML* Maximum likelihood: ML* Maximum likelihood: EM Maximum likelihood: EM Posterior estimation: EKF (SLAM) Posterior estimation: EKF (SLAM) Mapping: Outline

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Occupancy Grids: From scans to maps

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Occupancy Grid Maps Assumptions: poses known, occupancy binary, independent [Elfes/Moravec 88] Assume

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Example CAD map occupancy grid map The Tech Museum, San Jose

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping Algorithms - Comparison SLAM (Kalman) EMML*Occupan. Grids OutputPosteriorML/MAP Posterior ConvergenceStrongWeak?NoStrong Local minimaNoYes No Real timeYesNoYes Odom. ErrorUnbounded None Sensor NoiseGaussianAny # Features10 3  Feature uniqYesNo Raw dataNoYes

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Mapping: Lessons Learned n Concurrent mapping and localization: hard robotics problem n Best known algorithms are probabilistic 1.EKF/SLAM: Full posterior estimation, but restrictive assumptions (data association) 2.EM: Maximum Likelihood, solves data association 3.ML*: less robust but online 4.Occupancy grids: Binary Bayes filter, assumes known poses (= much easier)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 The Obvious Next Step EM for object mapping EM for concurrent localization

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Take-Home Message Mapping is the holy grail in mobile robotics. Every state-of-the-art mapping algorithm is probabilistic.

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Open Problems n 2D Indoor mapping and exploration n 3D mapping (real-time, multi-robot) n Object mapping (desks, chairs, doors, …) n Outdoors, underwater, planetary n Dynamic environments (people, retail stores) n Full posterior with data association (real-time, optimal)

Sebastian Thrun, Carnegie Mellon, IJCAI-2001 Open Problems, con’t n Mapping, localization n Control/Planning under uncertainty n Integration of symbolic making n Human robot interaction Literature Pointers: n “Robotic Mapping” at n “Probabilistic Robotics” AI Magazine 21(4)