Localization and Mapping Chapter 25.3 Chapter 25.3 1 Sensors - - PowerPoint PPT Presentation

Localization and Mapping

Chapter 25.3

Chapter 25.3 1

Sensors

Range finders: sonar (land, underwater), laser range finder, radar (aircraft), tactile sensors, GPS Imaging sensors: cameras (visual, infrared) Proprioceptive sensors: shaft decoders (joints, wheels), inertial sensors, force sensors, torque sensors

Chapter 25.3 2

Localization—Where Am I?

Compute current location and orientation (pose) given observations:

Xt+1 Xt At−2 At−1 At Zt−1 Xt−1 Zt Zt+1

Chapter 25.3 3

Localization contd.

xi, yi vt ∆t

t ∆t t+1

xt+1 h(xt) xt θt θ ω

Z1 Z2 Z3 Z4

Assume Gaussian noise in motion prediction, sensor range measurements

Chapter 25.3 4

Localization contd.

Can use particle filtering to produce approximate position estimate

Robot position Robot position Robot position

Chapter 25.3 5

Localization contd.

Can also use extended Kalman filter for simple cases:

robot landmark

Assumes that landmarks are identifiable—otherwise, posterior is multimodal

Chapter 25.3 6

Mapping

Localization: given map and observed landmarks, update pose distribution Mapping: given pose and observed landmarks, update map distribution SLAM: given observed landmarks, update pose and map distribution Probabilistic formulation of SLAM: add landmark locations L1, . . . , Lk to the state vector, proceed as for localization

Chapter 25.3 7

Mapping contd.

Chapter 25.3 8