Spring 2017 CIS 493, EEC 492, EEC 592: Autonomous Intelligent Robotics Instructor: Shiqi Zhang http://eecs.csuohio.edu/~szhang/teaching/17spring/
● About Assignment 2 ● About Proposal Draft (Due Feb 20, 5 PM ) ● Teaming: it’s your responsibility to convince people that you can contribute to the project ● Grading will be based on your proposal draft, proposal and final report ● Grading will be SUBJECTIVE ● Proposal presentation on March 9 ● Peer review
The SLAM Problem SLAM stands for simultaneous localization and mapping The task of building a map while estimating the pose of the robot relative to this map Why is SLAM hard? Chicken and egg problem: a map is needed to localize the robot and a pose estimate is needed to build a map Slides adapted from Probabilistic Robotics book 3
The SLAM Problem A robot moving though an unknown, static environment Given: – The robot’s controls – Observations of nearby features Estimate: – Map of features – Path of the robot 4
Why is SLAM a hard problem? SLAM : robot path and map are both unknown! Robot path error correlates errors in the map 5
Why is SLAM a hard problem? Robot pose uncertainty ● In the real world, the mapping between observations and landmarks is unknown ● Picking wrong data associations can have catastrophic consequences ● Pose error correlates data associations 6
Data Association Problem ● A data association is an assignment of observations to landmarks ● In general there are more than (n observations, m landmarks) possible associations ● Also called “assignment problem” 7
Particle Filters Represent belief by random samples Estimation of non-Gaussian, nonlinear processes Sampling Importance Resampling (SIR) principle Draw the new generation of particles Assign an importance weight to each particle Resampling T ypical application scenarios are tracking, localization, … 8
Localization vs. SLAM A particle fjlter can be used to solve both problems Localization: state space x, y, SLAM: state space x, y, , map for landmark maps = l 1 , l 2 , …, l m for grid maps = c 11 , c 12 , …, c 1n , c 21 , …, c nm Problem: The number of particles needed to represent a posterior grows exponentially with the dimension of the state space! 9
Dependencies Is there a dependency between the dimensions of the state space? If so, can we use the dependency to solve the problem more effjciently? 10
Dependencies Is there a dependency between the dimensions of the state space? If so, can we use the dependency to solve the problem more effjciently? In the SLAM context The map depends on the poses of the robot. We know how to build a map given the position of the sensor is known. 11
Factored Posterior (Landmarks) poses map observations & movements SLAM posterior Robot path posterior landmark positions Does this help to solve the problem? 12 Factorization first introduced by Murphy in 1999
Factored Posterior (Landmarks) poses map observations & movements 13 Factorization first introduced by Murphy in 1999
Mapping using Landmarks Landmark 1 l 1 z 1 z 3 observations . . . Robot poses x 0 x 1 x 2 x 3 x t controls u 1 u 1 u t-1 u 0 z 2 z t Landmark 2 l 2 Knowledge of the robot’s true path renders landmark positions conditionally independent 14
Factored Posterior Robot path posterior Conditionally (localization problem) independent landmark positions 15
Rao-Blackwellization This factorization is also called Rao-Blackwellization Given that the second term can be computed effjciently, particle fjltering becomes possible! 16
FastSLAM Rao-Blackwellized particle fjltering based on landmarks [Montemerlo et al., 2002] Each landmark is represented by a 2x2 Extended Kalman Filter (EKF) Each particle therefore has to maintain M EKFs Particle … x, y, Landmark 1 Landmark 2 Landmark M #1 Particle x, y, Landmark 1 Landmark 2 … Landmark M #2 … Particle x, y, Landmark 1 Landmark 2 Landmark M … 17 N
FastSLAM – Action Update Landmark #1 Filter Particle #1 Landmark #2 Filter Particle #2 Particle #3 18
FastSLAM – Sensor Update Landmark #1 Filter Particle #1 Landmark #2 Filter Particle #2 Particle #3 19
FastSLAM – Sensor Update Particle #1 Weight = 0.8 Particle #2 Weight = 0.4 Particle #3 Weight = 0.1 20
FastSLAM - Video 21 https://youtu.be/KqGXoaLGm08
Data Association Problem Which observation belongs to which landmark? ● A robust SLAM must consider possible data associations ● Potential data associations depend also on the pose of the robot 22
Multi-Hypothesis Data Association ● Data association is done on a per-particle basis ● Robot pose error is factored out of data association decisions 23
Per-Particle Data Association Was the observation generated by the red or the blue landmark? P(observation|red) = 0.3 P(observation|blue) = 0.7 T wo options for per-particle data association Pick the most probable match Pick an random association weighted by the observation likelihoods If the probability is too low, generate a new landmark 24
Results – Victoria Park ● 4 km traverse ● < 5 m RMS position error ● 100 particles Blue = GPS Yellow = FastSLAM 25 Dataset courtesy of University of Sydney
Results – Victoria Park 26 Dataset courtesy of University of Sydney
Grid-based SLAM Can we solve the SLAM problem if no pre-defjned landmarks are available? Can we use the ideas of FastSLAM to build grid maps? As with landmarks, the map depends on the poses of the robot during data acquisition If the poses are known, grid-based mapping is easy (“mapping with known poses”) 27
Mapping using Raw Odometry https://youtu.be/tilcwBVO4MY 28
Rao-Blackwellized Mapping Each particle represents a possible trajectory of the robot Each particle maintains its own map and updates it upon “mapping with known poses” Each particle survives with a probability proportional to the likelihood of the observations relative to its own map 29
Particle Filter Example 3 particles map of particle 3 map of particle 1 30 map of particle 2
Problem Each map is quite big in case of grid maps Since each particle maintains its own map Therefore, one needs to keep the number of particles small Solution : Compute better proposal distributions! Idea : Improve the pose estimate before applying the particle fjlter 31
Pose Correction Using Scan Matching Maximize the likelihood of the i-th pose and map relative to the (i-1)-th pose and map ˆ ˆ ˆ x arg max p ( z | x , m ) p ( x | u , x ) t t t t 1 t t 1 t 1 x t current measurement robot motion map constructed so far 32
Motion Model for Scan Matching Raw Odometry Scan Matching 33 https://youtu.be/sIMM73Was74
Conclusion ● The ideas of FastSLAM can also be applied in the context of grid maps ● Utilizing accurate sensor observation leads to good proposals and highly efficient filters ● It is similar to scan-matching on a per-particle base ● The number of necessary particles and re-sampling steps can seriously be reduced ● Improved versions of grid-based FastSLAM can handle larger environments than naïve implementations in “real time” since they need one order of magnitude fewer samples 34
Demo on a real robot 35
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