In this paper1 we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency, specially in...

In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measuremen...

This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on thought experiments and mathematical derivation. The first part highlights the structure of uncertainty of an estimated map with the key result being “Certainty of Relations despite Uncertainty of Positions”. A formal proof for approximate sparsity of so-called information...

Uncertainty quantification is a critical missing component in radio interferometric imaging that will only become increasingly important as the big-data era of radio interferometry emerges. Statistical sampling approaches to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling, can in principle recover the full posterior distribution of the image, from which uncertainties c...

Vision and proprioception contribute to generating hand movement. If a conflict between the visual and proprioceptive feedback of hand position is given, reaching movement is disturbed initially but recovers after training. Although previous studies have predominantly investigated the adaptive change in the motor output, it is unclear whether the contributions of visual and proprioceptive feedb...

This paper deals with the problems of map building and mobile robot localization. Ultrasonic sensors information, obtained as the robot moves, is integrated in order to build a map of the environment. This map is afterwards used for robot localization, correcting the errors that the dead reckoning system accumulates in long displacements. The approach focuses on the way to analyze sensor inform...

In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dime...

This paper presents a method for estimating uncertainty in MRI-based brain region delineations provided by fully-automated segmentation methods. In large data sets, the uncertainty estimates could be used to detect fully-automated method failures, identify low-quality imaging data, or endow downstream statistical analyses with per-subject uncertainty in derived morphometric measures. Region seg...

3D geological models commonly built to manage natural resources are much affected by uncertainty because most of the subsurface is inaccessible to direct observation. Appropriate ways to intuitively visualize uncertainties are therefore critical to draw appropriate decisions. However, empirical assessments of uncertainty visualization for decision making are currently limited to two-dimensional...

This paper defines a new method of attribute uncertainty representation, the blinking region. It specifically communicates uncertainty in values associated with choropleth map regions. In this way, error and attribute (census) data can be made to alternate in the user’s view, communicating both datasets. The blinking region uncertainty visualization was tested via a web-based survey. Results sh...