Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus

© 2017 IEEE. This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene

Monocular 3D metric scale reconstruction using depth from defocus and image velocity

© 2017 IEEE. This paper presents a novel approach to metric scale reconstruction of a three-dimensional (3D) scene using a monocular camera. Using a sequence of images from a monocular camera with a fixed focus lens, metric distance to a set of features in the environment is estimated from image blur due to defocus. The blur texture ambiguity which causes scale errors in depth from defocus is corrected in an EKF framework that exploits image velocity measurements. We show in real experiments that our method converges to a metric scale, accurate, sparse depth map and 3D camera poses with images from a monocular camera. Therefore, the proposed approach has the potential to enhance robot navigation algorithms that rely on monocular cameras

A Method to Include Antenna Pattern Characteristics in UWB System Design

In UWB system design and optimization, antennas are usually represented by a single transfer function relating the input voltage to the radiated electric field in the free space. However, the transfer function of planar ultra-wideband antennas depends on not only frequency but also direction. In this paper we present a strategy that helps the UWB system designer to select the best transfer function (and the best reference direction) for the antenna. In the process we demonstrate that good pattern stability of a UWB antenna within a particular band is advantageous to ease the complexities in the selection of the transfer function. We have thus emphasized the importance of having stable patterns for UWB antennas. Pulse optimization algorithms that meet FCC spectrum requirements are presented as examples

Stability of Graphene Oxide encapsulated Gold Nanorods for optical sensing purposes

This paper presents the synthesis and characterization of a graphene oxide encapsulated gold nanorod (GNR) complex, where its stability was investigated over time by recording the absorption spectra obtained using a UV/Visible spectrometer over the wavelength region of 200 nm to 1000 nm. Poly Ethylene Glycol (PEG) stablized GNRs were found to be more stable in the presence of graphene oxide dispersions compared to Cetyl Timethyl Ammonium Bromide (CTAB) stabilized GNRs. These GNR complexes, prepared with an active graphene oxide coating on the surface, are presented as a well-suited platform for the development of localized plasmon resonance-based fibre optic biosensors due to the surface functional groups of graphene oxide that can form bio-composites with other biological nanomaterials

Wheelchair driver assistance and intention prediction using POMDPs

Electric wheelchairs give otherwise immobile people the free-dom of movement, they significantly increase independence and dramatically increase quality of life. However the physical control systems of such wheelchair can be prohibitive for some users; for example, people with severe tremors. Several assisted wheelchair platforms have been developed in the past to assist such users. Algorithms that assist specific behaviors such as door - passing, follow - corridor, or avoid - obstacles have been successful. Recent research has seen a move towards systems that predict the users intentions, based on the users input. These predictions have been typically limited to locations immediately surrounding the wheelchair. This paper presents a new assisted wheelchair driving system with large scale intelligent intention recognition based on POMDPs (Partially Observable Markov Decision Processes). The systems acts as an intelligent agent/decision-maker, it relies on minimal user input; to predict the users intention and then autonomously drives the user to his destination. The prediction is constantly being updated as new user input is received allowing for true user/system integration. This shifts the users focus from fine motor-skilled control to coarse control intended to convey intention. © 2007 IEEE

A POMDP framework for modelling human interaction with assistive robots

This paper presents a framework for modelling the interaction between a human operator and a robotic device, that enables the robot to collaborate with the human to jointly accomplish tasks. States of the system are captured in a model based on a partially observable Markov decision process (POMDP). States representing the human operator are motivated by behaviours from the psychology of the human action cycle. Hierarchical nature of these states allows the exploitation of data structures based on algebraic decision diagrams (ADD) to efficiently solve the resulting POMDP. The proposed framework is illustrated using two examples from as-sistive robotics; a robotic wheel chair and an intelligent walking device. Experimental results from trials conducted in an office environment with the wheelchair is used to demonstrate the proposed technique. © 2011 IEEE

POMDP-based long-term user intention prediction for wheelchair navigation

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. Several navigational techniques have been successfully developed in the past to assist with specific behaviours such as "door passing" or "corridor following". These shared control strategies normally require the user to manually select the level of assistance required during use. Recent research has seen a move towards more intelligent systems that focus on forecasting users' intentions based on current and past actions. However, these predictions have been typically limited to locations immediately surrounding the wheelchair. The key contribution of the work presented here is the ability to predict the users' intended destination at a larger scale, that of a typical office arena. The systems relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently drive the user to his destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users' focus from fine motor-skilled control to coarse control broadly intended to convey intention. Successful simulation and experimental results on a real wheelchair robot demonstrate the validity of the approach. ©2008 IEEE

Intention driven assistive wheelchair navigation

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. The system has the ability to predict the users intended destination at a larger scale, that of a typical office or home arena. This system relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently aid in driving the user to the destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users focus from fine motor-skilled control to coarse guidance, broadly intended to convey intention. Successful simulation and experimental results on a real automated wheelchair platform demonstrate the validity of the approach