FYP Presentation

Monocular Simultaneous Localisation and Mapping (SLAM)

Xuechen LiuSupervisor: Simon

MaskellAssessor: Richard

WilliamsDepartment of Electrical Engineering and Electronics

University of LiverpoolMail: [email protected]

What is SLAM: Chicken or Egg

Xuechen Liu [email protected]

A map is asked to be built for localization……

While the pose of the robot need to be estimated based on accurate mapping

Where am I?

What surrounds me?

SLAM Market


Autonomous vehicles

Portable Devices

Conventional Approach: KF Based


All Gaussian……N !

All Linear!Motion

Measurement

Background: Particle Filter & FastSLAM2.0


Generic Particle Filter

Prediction

Weight Assignment

Resampling

Iterative Process Essence of

FastSLAM2.0Through

Particles

Contribution: UKF Replaces EKF


Both two Filters can model non-linear distribution, but……EKF(Extended Kalman Filter)

UKF(Unscented Kalman Filter)

V.

S

Taylor Expansion

Unscented Transform

UKF has

BETTER estimation than EKF

UKF: Better Estimation, Easier Understanding, Harder Implementation!

Contribution: Prior Vision System


Generally Speaking……Read Video

Corner Detection & Matching

Reconstruct 3D points

Pose EstimationFAST

Corner Detection

Structure from Motion

Final System: How it Will Work?


WE WILL SEE.

Further Actions


1) Implement the whole system

2) Gaussian Removal: Get the exact expression of weights back

3) Student T likelihood model application [optional]

They can work well separately, so how about the combination?

Literature Review/References[1] M. Montemerlo, S. Thrun, D. Koller and B. Wehbreit, "FastSLAM: A Factored Solution to the

Simultaneous Localization and Mapping Problem," 2002.

[2] M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit, "FastSLAM2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges," in International Joint Conference on Artificial Intelligence, 2003.

[3] M. Arulampalam, S. Maskell, N. Gordon and T. Clapp, “A Tutorial on particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-187, 2002.

[4] F. Dellaert, D. Fox, W. Burgard and S. Thrun, "Monte Carlo Localization for Mobile Robots," in International Conference on Robotics & Automation, Detroit, Michigan, 1999.

[5] H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, “Speed-Up Robust Features (SURF),” Computer Vision and Image Understanding, no. 110, pp. 346-359, 2007.

[6] E. Rosten, R. Porter and T. Drummond, "Faster and Better: A Machine Learning Approach to Corner Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 105-119, 2010.

[7] S. Julier and J. Uhlmann, “Unscented Filtering and Nonlinear Estimation,” Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.


Thanks for Listening!Any Questions?


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