Affordance-map: Mapping human context in 3D scenes using cost-sensitive SVM and virtual human models

© 2015 IEEE. Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human robot interaction. Even when humans are present in the environment, detecting their presence in cluttered environments could be challenging. As a solution to this problem, this paper presents the concept of affordance-map which learns human context by looking at geometric features of the environment. Instead of observing real humans to learn human context, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes. The affordance-map learning problem is formulated as a multi label classification problem that can be learned using cost-sensitive SVM. Experiments carried out in a real 3D scene dataset recorded promising results and proved the applicability of affordance-map for mapping human context

Active visual object search using affordance-map in real world: A human-centric approach

© 2014 IEEE. Human context is the most natural explanation why objects are placed and arranged in a particular order in an indoor environment. Usually, humans arrange objects in order to support their intended activities in a given environment. However, most of the common approaches for robotic object search involve modelling object-object relationships. In this paper, we hypothesize such relationships are centered around humans and bring human context to object search by modelling human-objects relationships through affordance-map. It identifies locations in a 3D map which support a particular affordance using virtual human models. Therefore, our approach does not require to observe real humans in the scene. The affordance-map and object-human-robot relationship are then used to infer the object search strategy. We tested our algorithm using a mobile robot that actively searched for the object 'computer monitors' in an office environment with promising results

Affordance-map: A map for context-aware path planning

'Context-awareness' could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with humans co-workers. Arguably, a robot with appropriate context-awareness could lead to a better human robot interaction. In this paper, we address the problem of combining contextawareness with robotic path planning. Our approach is based on affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn human context in an given environment without observing real human behaviours which themselves are a non-trivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop a context-aware global path planning strategy by using the well known A∗ algorithm. The proposed method was tested in a real office environment and proved our algorithm is capable of moving a robot in a path that minimises the distractions to human co-workers

Some convolution and scale transformation techniques to enhance GPR images

© 2019 IEEE. Locating reinforcement rods embedded inside concrete wall-like structures, as well as locating subsurface features such as voids, cracks, and interfaces is an essential part of structural health monitoring of concrete infrastructure. The Ground Penetrating Radar (GPR) technique has been commonly used as a means of Non-destructive Testing and Evaluation (NDT E) which suits the purpose. In the recent past, the interest of using GPR to assess the crowns (i.e., top) of concrete sewers has been rising. Moisture is well known to be a challenge for GPR imaging as moisture tends to influence GPR waves. This challenge becomes more common and persistent inside sewers since sewer walls contain considerable surface and subsurface moisture as a result of the humid environment created by the waste water flowing through sewers as well as the bacteria and gas induced acid attacks. Forming a part of sewer condition assessment-related research with the objective of assessing moist concrete, this paper presents some preliminary results which demonstrate how some simple scale transformations and convolution can help in enhancing GPR images in grey-scale. A set of raw GPR signals captured on a moist concrete block inside a laboratory environment is considered. The effect of enhancement is demonstrated against a benchmark image constructed by mapping the raw signals directly onto grey-scale

Frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete

© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Electrical resistivity is an important parameter to be monitored for the conditional assessment and health monitoring of aging and new concrete infrastructure. In this paper, we report the design and development of a frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete. Firstly, a sensing system prototype was developed based on the Wenner probe arrangement for the electrical resistivity measurements. This system operates by integrating three major units namely current injection unit, sensing unit and microcontroller unit. Those units govern the overall operations of the sensing system. Secondly, the measurements from the developed unit were compared with the measurements of the commercially available device at set conditions. This experimentation evaluated the measurement performance and demonstrated the effectiveness of the developed sensor prototype. Finally, the influence of rebar and the effect of frequency on the electrical measurements were studied through laboratory experimentation on a concrete sample. Experimental results indicated that the electrical resistivity measurements taken at a closer proximity to the rebar had its influence than the measurements taken away from the rebar in the ideal set condition. Also, the increase in electrical resistivity to the increase in frequency was observed, and then the measurements show lesser variations to higher frequency inputs

Capacitive Sensor Based 2D Subsurface Imaging Technology for Non Destructive Evaluation of Building Surfaces

Understanding the underlying structure of building surfaces like walls and floors is essential when carrying out building maintenance and modification work. To facilitate such work, this paper introduces a capacitive sensor-based technology which can conduct non-destructive evaluation of building surfaces. The novelty of this sensor is that it can generate a real-time 2D subsurface image which can be used to understand structure beneath the top surface. Finite Element Analysis (FEA) simulations are done to understand the best sensor head configuration that gives optimum results. Hardware and software components are custom-built to facilitate real-time imaging capability. The sensor is validated by laboratory tests, which revealed the ability of the proposed capacitive sensing technology to see through common building materials like wood and concrete. The 2D image generated by the sensor is found to be useful in understanding the subsurface structure beneath the top surface

Learning Hidden Human Context in 3D Office Scenes by Mapping Affordances Through Virtual Humans

© 2015 World Scientific Publishing Company. Ability to learn human context in an environment could be one of the most desired fundamental abilities that a robot should have when sharing a workspace with human co-workers. Arguably, a robot with appropriate human context awareness could lead to a better human-robot interaction. In this paper, we address the problem of learning human context in an office environment by only using 3D point cloud data. Our approach is based on the concept of affordance-map, which involves mapping latent human actions in a given environment by looking at geometric features of the environment. This enables us to learn the human context in the environment without observing real human behaviors which themselves are a nontrivial task to detect. Once learned, affordance-map allows us to assign an affordance cost value for each grid location of the map. These cost maps are later used to develop an active object search strategy and to develop a context-aware global path planning strategy

Multi-camera visual odometry for skid steered field robot

Position estimation using wheel odometric systems tends to give rather poor performances for an outdoor fourwheel skid steered mobile robot. Therefore autonomous control of these vehicles is extremely challenging in outdoor environments. This paper describes an outdoor localization system based on visual odometry for skid steered vehicle using forward faced camera and a downward faced camera. Optical flow field data is statistically analyzed to correctly estimate the position of the robot. Kalman Filtering is used to fuse data from two cameras for optimum performance. Also real-time Instantaneous Center of Rotation (ICR) detection using optical flow field data is proposed to calculate the heading angle. Two consumer grade cameras were used and algorithm was tested using open source image processing libraries. The proposed system yielded an acceptable positioning accuracy on short runs in typical outdoor terrains

An Experimental study on using visual odometry for short-run self localization of field robot

one of the most challenging problems of field robots is self-localization, which involves incremental update of position while in motion. Though wheel based odometry is cheaper to implement its accuracy degrades when wheels slip. In this paper performance of low-cost visual odometry approach is experimented as a feasibility test for field robot localization. We have used a downward-facing camera and tested localization error in view of various parameters such as frame size, frame rate, etc. A FFT-based image registration techniques was utilized to determine the precise translation distances and heading between consecutive frames captured from ground surface. Basic navigation experiments, including a loop-closing test and error propagation were conducted and interesting numerical results have been reported

Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features

Ability to recognize human activities will enhance the capabilities of a robot that interacts with humans. However automatic detection of human activities could be challenging due to the individual nature of the activities. In this paper, we present human activity detection model that uses only 3-D skeleton features generated from an RGB-D sensor (Microsoft Kinect TM). To infer the human activities, we implemented Gaussian Mixture Modal (GMM) based Hidden Markov Model(HMM). GM outputs of the HMM were effectively able to capture multimodel nature of 3D positions of each skeleton joint. We test our model in a publicly available data-set that consists of twelve different daily activities performed by four different people. The proposed model recorded recognition recall accuracy of 84% with previously seen people and 78% with previously unseen people. © 2013 IEEE