The fusion of Iterative Closest Point (ICP) registrations in existing state estimation
frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the …

This letter proposes a new image-based localization framework that explicitly localizes the
camera/robot by fusing Convolutional Neural Network (CNN) and sequential images' …

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states
learned indirectly by optimization of a supervised, unsupervised, or reinforcement training …

We develop a model by choosing the maximum entropy distribution from the set of models
satisfying certain smoothness and independence criteria; we show that inference on this …

Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization
and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper …

Many algorithms in computer vision and robotics make strong assumptions about
uncertainty, and rely on the validity of these assumptions to produce accurate and consistent …

Outdoor applications for small-scale Unmanned Aerial Vehicles (UAVs) commonly rely on
Global Positioning System (GPS) receivers for continuous and accurate position estimates …

Navigation in unknown, chaotic environments continues to present a significant challenge
for the robotics community. Lighting changes, self-similar textures, motion blur, and moving …

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators
based on active inference. In the majority of existing schemes a binary decision of whether a …

The ubiquitous additive Gaussian noise model is favored in statistical modeling applications
for its flexibility and ease of use. Often noise is assumed to be well-represented by a …