Map-Based Localization approaches use a local map of the sensed environment that is matched against a previously stored map to correct the robot localization in the world. In many cases these methods are based on a probabilistic representation of the spatial uncertainty and use the Kalman Filter (KF) or the Extended Kalman Filter (EKF) to update the robot’s location estimation. On the other han...

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates also provide visual attention maps. Particularly, solve for question answering We incorporate modern probabilistic methods further improve by using the gradients these estimates. These have two-fold benefits: a) improvement in obtaining corre...

soil erosion models are useful tools to predict runoff, sediment and soil erosion in watersheds. although, the swat model is used for evaluating runoff discharge and the long-term effects of management operation on water, sediment and agricultural chemical yields in the large watersheds, in this research, its efficiency was investigated in monthly runoff simulation for tuyserkan watershed (with...

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize loca...

In this paper, a new method of information fusion – DSmT (Dezert and Smarandache Theory) is introduced to apply to managing and dealing with the uncertain information from robot map building. Here we build grid map form sonar sensors and laser range finder (LRF). The uncertainty mainly comes from sonar sensors and LRF. Aiming to the uncertainty in static environment, we propose Classic DSm (DSm...

An online map building evolutionary algorithm is proposed using multi-agent mobile robots with odometric uncertainty. The control algorithm for map building in each robot is identical and trained by online evolutionary algorithm (EA). Each robot has configuration uncertainty which increases as it moves, and it perceives the surrounding environment information by the limited range sensors. It co...

Geologic maps are products of complex analyses and interpretations, and as such, contain a certain amount of uncertainty. Unfortunately, it is difficult to estimate the uncertainty within a geologic map. Much of this difficulty is due to the lack of a clear framework for describing the sources of uncertainty within the map and poor awareness, within the mapping community, of methods capable of ...

This paper presents a method which reduces uncertainty of a position and a direction of an autonomous robot by observing environment with a camera. In the proposed method, the state of the robot is represented by a state vector obeying a probability distribution. The robot creates and renews an environment map by considering the information from the mounted camera. Moreover, the robot revises t...

Rao–Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments...