Information-driven 6D SLAM based on ranging vision

This paper presents a novel solution for building three-dimensional dense maps in unknown and unstructured environment with reduced computational costs. This is achieved by giving the robot the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation of the robot location and its surroundings. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal and is capable of handling large data sets collected at realistic sampling rates. The strategy is generic to any 3D feature-based simultaneous localization and mapping (SLAM) algorithm in the information form, but in the work presented here it is closely coupled to a proposed novel appearance-based sensory package. It consists of a conventional camera and a range imager, which provide range, bearing and elevation inputs to visual salient features as commonly used by three-dimensional point-based SLAM, but it is also particularly well adapted for lightweight mobile platforms such as those commonly employed for Urban Search and Rescue (USAR), chosen here to demonstrate the excellences of the proposed strategy. ©2008 IEEE

Information efficient 3D visual SLAM in unstructured domains

This paper presents a strategy for increasing the efficiency of simultaneous localisation and mapping (SLAM) in unknown and unstructured environments using a vision-based sensory package. Traditional feature-based SLAM, using either the Extended Kalman Filter (EKF) or its dual, the Extended Information Filter (EIF), leads to heavy computational costs while the environment expands and the number of features increases. In this paper we propose an algorithm to reduce computational cost for real-time systems by giving robots the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal. © 2007 IEEE

An efficient path planner for large mobile platforms in cluttered environments

This paper presents a one step smooth and efficient path planning algorithm for navigating a large robotic platform in known cluttered environments. The proposed strategy, based on the generation of a novel search space, relies on non-uniform density sampling of the free areas to direct the computational resources to troubled and difficult regions, such as narrow passages, leaving the larger open spaces sparsely populated. A smoothing penalty is also associated to the nodes to encourage the generation of gentle paths along the middle of the empty spaces. Collision detection is carried out off-line during the creation of the configuration space to speed up the actual search for the path, which is done on-line. Results prove that the proposed approach considerably reduces the search space in a meaningful and practical manner, improving the computational cost of generating a path optimised for fine and smooth motion. © 2006 IEEE

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

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

Vision-based SLAM using natural features in indoor environments

This paper presents a practical approach to solve the simultaneous localization and mapping (SLAM) problem for autonomous mobile platforms by using natural visual landmarks obtained from an stereoscopic camera. It is an attempt to depart from traditional sensors such as laser rangefinders in order to gain the many benefits of nature-inspired information-rich 3D vision sensors. Whilst this makes the system fully observable in that the sensor provide enough information (range and bearing) to compute the full 2D estate of the observed landmarks from a single position, it is also true that depth information is difficult to rely on, particularly on measurements beyond a few meters (in fact the full 3D estate is observable, but here robot motion is constrained to 2D and only the 2D problem is considered). The work presented here is an attempt to overcome such a drawback by tackling the problem from a partially measurable SLAM perspective in that only landmark bearing from one of the cameras is employed in the fusion estimation. Range information estimates from the stereo pair is only used during map building in the landmark initialization phase in order to provide a reasonably accurate initial estimate. An additional benefit of the approach presented here lies in the data association aspect of SLAM. The availability of powerful feature extraction algorithms from the vision community, such as SIFT, permits a more flexible SLAM implementation separated from feature representation, extraction and matching, essentially carrying out matching with minimal recourse to geometry. Simulation results on real data illustrate the validity of the approach. © 2005 IEEE

A kyno-dynamic metric to plan stable paths over uneven terrain

A generic methodology to plan increasingly stable paths for mobile platforms travelling over uneven terrain is proposed in this paper. This is accomplished by extending the Fast Marching level-set method of propagating interfaces in 3D lattices with an analytical kyno-dynamic metric which embodies robot stability in the given terrain. This is particularly relevant for reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as robots equipped with manipulator arms or varying traction arrangements. Results obtained from applying the proposed strategy in a mobile rescue robot operating on simulated and real terrain data illustrate the validity of the proposed strategy. ©2010 IEEE

Extending the limits of feature-based SLAM with B-splines

This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments. © 2009 IEEE

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

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