Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Computing MAP trajectories by representing, propagating and combining PDFs over groups
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning gas distribution models using sparse Gaussian process mixtures
Autonomous Robots
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Gaussian process modeling of large-scale terrain
Journal of Field Robotics - Three-Dimensional Mapping, Part 1
Sliding window filter with application to planetary landing
Journal of Field Robotics - Visual Mapping and Navigation Outdoors
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Linear operators and stochastic partial differential equations in Gaussian process regression
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
iSAM2: Incremental smoothing and mapping using the Bayes tree
International Journal of Robotics Research
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Online Sparse Gaussian Process Regression and Its Applications
IEEE Transactions on Image Processing
Gaussian Process Gauss-Newton: Non-Parametric State Estimation
CRV '12 Proceedings of the 2012 Ninth Conference on Computer and Robot Vision
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In this paper, we present Gaussian Process Gauss-Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. This work adapts the methods of Gaussian process (GP) regression to address the problem of batch simultaneous localization and mapping (SLAM) by using the Gauss-Newton optimization method. In particular, we formulate the estimation problem with a continuous-time state model, along with the more conventional discrete-time measurements. Two derivations are presented in this paper, reflecting both the weight-space and function-space approaches from the GP regression literature. Validation is conducted through simulations and a hardware experiment, which utilizes the well-understood problem of two-dimensional SLAM as an illustrative example. The performance is compared with the traditional discrete-time batch Gauss-Newton approach, and we also show that GPGN can be employed to estimate motion with only range/bearing measurements of landmarks (i.e. no odometry), even when there are not enough measurements to constrain the pose at a given timestep.