علي حسين عبد الزهرة الدباغ

Vision based systems for terrain detection play important roles in mobile robotics, and recently such systems emerged for locomotion assistance of disabled people. For instance, they can be used as wearable devices to assist blind people or to guide prosthesis or exoskeleton controller to retrieve gait patterns being adapted to the executed task (overground walking, stairs, slopes, etc.). In this paper, we present a computer vision-based algorithm achieving the detection of flat ground, steps, and ramps using a depth camera. Starting from point cloud data collected by the camera, it classifies the environment as a function of extracted features. We further provide a pilot validation in an indoor environment containing a rich set of different types of terrains, even with partial occlusion. The paper further shows that our system needs less computational resources than recently published concurrent approaches, owing to the original transformation method we developed.

Abstract—In this paper, we explore a new algorithm to detect people with color cameras based on our modified Implicit Shape Model (ISM) implemented for grey scale thermal images. The idea of this approach is to convert a color image to a grey scale image, invert it to appear like a thermal image, and then apply the same algorithm that we used for people detection in thermal images. As the first step, we use the ISM to define the proposed centers of people locations. Then we utilize a novel method to detect people based on the density of the concentrated proposed centers by using an auto generated threshold mechanism. Our method is easy to implement and does not require complicated computations; thus resulting in a considerable increase in the speed performance and decrease in the cost of the required hardware on mobile platforms. We evaluated our system by testing it on three image sets for indoor and three for outdoor scenarios. Our system showed promising results in detecting people on images taken by different types of color cameras under difficult scenarios. This technique is used as the vision system for a rescue assist mobile robot built at Flinders University.

Abstract: In this paper, we explore a new algorithm to detect people with thermal cameras based on the standard Implicit Shape Model (ISM) technique. Our approach starts with the ISM to define the proposed centers of people locations. Then we utilize a novel method to detect people based on the density of the concentrated proposed centers by using an auto generated threshold mechanism. Our method is easy to implement and does not require complicated computations; thus resulting in a considerable increase in the speed performance and decrease in the cost of the required hardware on mobile platforms. We evaluated our system by testing it on three image sets for indoor and three for outdoor scenarios taken from four databases. Our system showed promising results in detecting people on images taken by different types of thermal cameras under difficult scenarios. This technique will be used as the vision system for a rescue assist mobile robot currently being built at Flinders University.

Terrain detection systems have been developed for a large body of applications. For instance, a bionic leg prosthesis would have to adapt its behavior as a function of the terrain, in order to restore a sound lower-limb biomechanics to the amputee. Visually impaired people benefit from such systems in order to collect information about their locomotion environment and avoid obstacles. Finally, mobile robots use them for estimating terrain traversability, and adjusting control algorithms as a function of the surface type. This diversity of applications led to a large repertoire of systems, regarding both hardware (sensors, processing unit) and software used for classification. This paper provides an extended review of these systems, with a specific focus on the assistance of disabled walker. More precisely, it overviews the sensory systems and algorithms that were implemented to identify different locomotion terrains in indoor or urban environments (flat ground, stairs, slopes) in a way that they are or can be worn by a human user, and running in real-time. Contributions from mobile robotics are also included, pending that they could be adapted to a scenario of locomotion assistance. The systems are classified into two categories: these relying on proprioceptive sensors only and those further using exteroceptive sensors. Contributions from both categories are then compared according to their main specifications, such as accuracy and prediction time. The paper unambiguously shows that systems with exteroceptive sensors have higher prediction capability than systems with proprioceptive sensors only, and should thus be favored for assistive devices requiring predicting transitions between locomotion tasks.