Joao Pedro Hespanha

US Patent:

20190242710, Aug 8, 2019

Filed:

Apr 16, 2019

Appl. No.:

16/385641

Inventors:

- Oakland CA, US
Jason T. Isaacs - Santa Barbara CA, US
Francois Quitin - Brussels, BE
Joao P. Hespanha - Santa Barbara CA, US
Upamanyu Madhow - Santa Barbara CA, US

International Classification:

G01C 21/00
G01S 19/22

Abstract:

Various embodiments each include at least one of systems, methods, devices, and software for GNSS simultaneous localization and mapping (SLAM). The disclosed techniques demonstrate that simultaneous localization and mapping (SLAM) can be performed using only GNSS SNR and geo-location data, collectively termed GNSS data henceforth. A principled Bayesian approach for doing so is disclosed. A 3-D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. Loopy Belief Propagation is used to estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location.

US Patent:

20160290805, Oct 6, 2016

Filed:

Dec 2, 2014

Appl. No.:

15/038702

Inventors:

- Oakland CA, US
Jason T. Isaacs - Santa Barbara CA, US
Francois Quitin - Brussels, BE
Joao P. Hespanha - Santa Barbara CA, US
Upamanyu Madhow - Santa Barbara CA, US

Assignee:

THE REGENTS OF UNIVERSITY OF CALIFORNIA - Oakland CA

International Classification:

G01C 21/00
G01S 19/22

Abstract:

Various embodiments each include at least one of systems, methods, devices, and software for GNSS simultaneous localization and mapping (SLAM). The disclosed techniques demonstrate that simultaneous localization and mapping (SLAM) can be performed using only GNSS SNR and geo-location data, collectively termed GNSS data henceforth. A principled Bayesian approach for doing so is disclosed. A 3-D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. Loopy Belief Propagation is used to estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location.