Showing papers presented at "Field and Service Robotics in 2016"

Autonomous Exploration for Infrastructure Modeling with a Micro Aerial Vehicle

Abstract: Micro aerial vehicles (MAVs) are an exciting technology for mobile sensing of infrastructure as they can easily position sensors in to hard to reach positions. Although MAVs equipped with 3D sensing are starting to be used in industry, they currently must be remotely controlled by a skilled pilot. In this paper we present an exploration path planning approach for MAVs equipped with 3D range sensors like lidar. The only user input that our approach requires is a 3D bounding box around the structure. Our method incrementally plans a path for a MAV to scan all surfaces of the structure up to a resolution and detects when exploration is finished. We demonstrate our method by modeling a train bridge and show that our method builds 3D models with the efficiency of a skilled pilot.

Application of Multi-Robot Systems to Disaster-Relief Scenarios with Limited Communication

Abstract: In this systems description paper, we present a multi-robot solution for intelligence-gathering tasks in disaster-relief scenarios where communication quality is uncertain. First, we propose a formal problem statement in the context of operations research. The hardware configuration of two heterogeneous robotic platforms capable of performing experiments in a relevant field environment and a suite of autonomy-enabled behaviors that support operation in a communication-limited setting are described. We also highlight a custom user interface designed specifically for task allocation amongst a group of robots towards completing a central mission. Finally, we provide an experimental design and extensive, preliminary results for studying the effectiveness of our system.

Vision and Learning for Deliberative Monocular Cluttered Flight

Abstract: Cameras provide a rich source of information while being passive, cheap and lightweight for small Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. Two key contributions make this possible: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with an off-the-shelf quadrotor. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar.

Long-Endurance Sensing and Mapping Using a Hand-Launchable Solar-Powered UAV

Abstract: This paper investigates and demonstrates the potential for very long endurance autonomous aerial sensing and mapping applications with AtlantikSolar, a small-sized, hand-launchable, solar-powered fixed-wing unmanned aerial vehicle. The platform design as well as the on-board state estimation, control and path-planning algorithms are overviewed. A versatile sensor payload integrating a multi-camera sensing system, extended on-board processing and high-bandwidth communication with the ground is developed. Extensive field experiments are provided including publicly demonstrated field-trials for search-and-rescue applications and long-term mapping applications. An endurance analysis shows that AtlantikSolar can provide full-daylight operation and a minimum flight endurance of 8 h throughout the whole year with its full multi-camera mapping payload. An open dataset with both raw and processed data is released and accompanies this paper contribution.

Large Scale Dense Visual Inertial SLAM

Abstract: In this paper we present a novel large scale SLAM system that combines dense stereo vision with inertial tracking. The system divides space into a grid and efficiently allocates GPU memory only when there is surface information within a grid cell. A rolling grid approach allows the system to work for large scale outdoor SLAM. A dense visual inertial dense tracking pipeline incrementally localizes stereo cameras against the scene. The proposed system is tested with both a simulated data set and several real-life data in different lighting (illumination changes), motion (slow and fast), and weather (snow, sunny) conditions. Compared to structured light-RGBD systems the proposed system works indoors and outdoors and over large scales beyond single rooms or desktop scenes. Crucially, the system is able to leverage inertial measurements for robust tracking when visual measurements do not suffice. Results demonstrate effective operation with simulated and real data, and both indoors and outdoors under varying lighting conditions.

System Design of a Tethered Robotic Explorer (TReX) for 3D Mapping of Steep Terrain and Harsh Environments

Abstract: The use of a tether in mobile robotics provides a method to safely explore steep terrain and harsh environments considered too dangerous for humans and beyond the capability of standard ground rovers. However, there are significant challenges yet to be addressed concerning mobility while under tension, autonomous tether management, and the methods by which an environment is assessed. As an incremental step towards solving these problems, this paper outlines the design and testing of a center-pivoting tether management payload enabling a four-wheeled rover to access and map steep terrain. The chosen design permits a tether to attach and rotate passively near the rover’s center-of-mass in the direction of applied tension. Prior design approaches in tethered climbing robotics are presented for comparison. Tests of our integrated payload and rover, Tethered Robotic Explorer (TReX), show full rotational freedom while under tension on steep terrain, and basic autonomy during flat-ground tether management. Extensions for steep-terrain tether management are also discussed. Lastly, a planar lidar fixed to a tether spool is used to demonstrate a 3D mapping capability during a tethered traverse. Using visual odometry to construct local point-cloud maps over short distances, a globally-aligned 3D map is reconstructed using a variant of the Iterative Closest Point (ICP) algorithm.

Beyond a Shadow of a Doubt: Place Recognition with Colour-Constant Images

Abstract: Colour-constant images have been shown to improve visual navigation taking place over extended periods of time. These images use a colour space that aims to be invariant to lighting conditions—a quality that makes them very attractive for place recognition, which tries to identify temporally distant image matches. Place recognition after extended periods of time is especially useful for SLAM algorithms, since it bounds growing odometry errors. We present results from the FAB-MAP 2.0 place recognition algorithm, using colour-constant images for the first time, tested with a robot driving a 1 km loop 11 times over the course of several days. Computation can be improved by grouping short sequences of images and describing them with a single descriptor. Colour-constant images are shown to improve performance without a significant impact on computation, and the grouping strategy greatly speeds up computation while improving some performance measures. These two simple additions contribute robustness and speed, without modifying FAB-MAP 2.0.

A Stereo Vision Based Obstacle Detection System for Agricultural Applications

Abstract: In this paper, an obstacle detection system for field applications is presented which relies on the output of a stereo vision camera. In a first step, it splits the point cloud into cells which are analyzed in parallel. Here, features like density and distribution of the points and the normal of a fitted plane are taken into account. Finally, a neighborhood analysis clusters the obstacles and identifies additional ones based on the terrain slope. Furthermore, additional properties can be easily derived from the grid structure like a terrain traversability estimation or a dominant ground plane. The experimental validation has been done on a modified tractor on the field, with a test vehicle on the campus and within the forest.

Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers

Abstract: This paper explores the use of unmanned aerial vehicles (UAVs) in wildfire monitoring. To begin establishing effective methods for autonomous monitoring, a simulation (FLAME) is developed for algorithm testing. To simulate a wildfire, the well established FARSITE fire simulator is used to generate realistic fire behavior models. FARSITE is a wildfire simulator that is used in the field by Incident Commanders (IC’s) to predict the spread of the fire using topography, weather, wind, moisture, and fuel data. The data obtained from FARSITE is imported into FLAME and parsed into a dynamic frontier used for testing hotspot monitoring algorithms. In this paper, points of interest along the frontier are established as points with a fireline intensity (British-Thermal-Unit/feet/second) above a set threshold. These interest points are refined into hotspots using the Mini-Batch K-means Clustering technique. A distance threshold differentiates moving hotspot centers and newly developed hotspots. The proposed algorithm is compared to a baseline for minimizing the sum of the max time untracked J(t). The results show that simply circling the fire performs poorly (baseline), while a weighted-greedy metric (proposed) performs significantly better. The algorithm was then run on a UAV to demonstrate the feasibility of real world implementation.

What Do You Have to Offer Me?”: A Relationship Building Activity for Demonstrating Social Exchange Theory

Abstract: In this article, we describe an interactive classroom activity designed to help students encounter social exchange theory in action. During the “What Do You Have to Offer Me?” exercise, each student selected seven cards, each containing a characteristic related to personality, physical characteristics, family history, finances, ideology, and occupation. Students were then asked to mill around the room and find someone with whom they would be interested in developing a relationship, based on the assigned characteristics. Once all students found a partner and were seated, students reflected on the process of the activity, as well as its application to social exchange theory. In addition to providing details of the activity, we conclude with student reflections and evaluative data on the exercise.

In the Dead of Winter: Challenging Vision-Based Path Following in Extreme Conditions

Abstract: In order for vision-based navigation algorithms to extend to long-term autonomy applications, they must have the ability to reliably associate images across time. This ability is challenged in unstructured and outdoor environments, where appearance is highly variable. This is especially true in temperate winter climates, where snowfall and low sun elevation rapidly change the appearance of the scene. While there have been proposed techniques to perform localization across extreme appearance changes, they are not suitable for many navigation algorithms such as autonomous path following, which requires constant, accurate, metric localization during the robot traverse. Furthermore, recent methods that mitigate the effects of lighting change for vision algorithms do not perform well in the contrast-limited environments associated with winter. In this paper, we highlight the successes and failures of two state-of-the-art path-following algorithms in this challenging environment. From harsh lighting conditions to deep snow, we show through a series of field trials that there remain serious issues with navigation in these environments, which must be addressed in order for long-term, vision-based navigation to succeed.

BOR$$^$$G: Building Optimal Regularised Reconstructions with GPUs (in Cubes)

Abstract: This paper is about dense regularised mapping using a single camera as it moves through large work spaces. Our technique is, as many are, a depth-map fusion approach. However, our desire to work both at large scales and outdoors precludes the use of RGB-D cameras. Instead, we need to work with the notoriously noisy depth maps produced from small sets of sequential camera images with known inter-frame poses. This, in turn, requires the application of a regulariser over the 3D surface induced by the fusion of multiple (of order 100) depth maps. We accomplish this by building and managing a cube of voxels. The combination of issues arising from noisy depth maps and moving through our workspace/voxel cube, so it envelops us, rather than orbiting around it as is common in desktop reconstructions, forces the algorithmic contribution of our work. Namely, we propose a method to execute the optimisation and regularisation in a 3D volume which has been only partially observed and thereby avoiding inappropriate interpolation and extrapolation. We demonstrate our technique indoors and outdoors and offer empirical analysis of the precision of the reconstructions.

Wrong Today, Right Tomorrow: Experience-Based Classification for Robot Perception

Abstract: This paper is about building robots that get better through use in their particular environment, improving their perceptual abilities. We approach this from a life long learning perspective: we want the robot’s ability to detect objects in its specific operating environment to evolve and improve over time. Our idea, which we call Experience-Based Classification (EBC), builds on the well established practice of performing hard negative mining to train object detectors. Rather than cease mining for data once a detector is trained, EBC continuously seeks to learn from mistakes made while processing data observed during the robot’s operation. This process is entirely self-supervised, facilitated by spatial heuristics and the fact that we have additional scene data at our disposal in mobile robotics. In the context of autonomous driving we demonstrate considerable object detector improvement over time using 40 Km of data gathered from different driving routes at different times of year.

Monocular Visual Teach and Repeat Aided by Local Ground Planarity

Abstract: Visual Teach and Repeat (VT&R) allows an autonomous vehicle to repeat a previously traversed route without a global positioning system. Existing implementations of VT&R typically rely on 3D sensors such as stereo cameras for mapping and localization, but many mobile robots are equipped with only 2D monocular vision for tasks such as teleoperated bomb disposal. While simultaneous localization and mapping (SLAM) algorithms exist that can recover 3D structure and motion from monocular images, the scale ambiguity inherent in these methods complicates the estimation and control of lateral path-tracking error, which is essential for achieving high-accuracy path following. In this paper, we propose a monocular vision pipeline that enables kilometre-scale route repetition with centimetre-level accuracy by approximating the ground surface near the vehicle as planar (with some uncertainty) and recovering absolute scale from the known position and orientation of the camera relative to the vehicle. This system provides added value to many existing robots by allowing for high-accuracy autonomous route repetition with a simple software upgrade and no additional sensors. We validate our system over 4.3 km of autonomous navigation and demonstrate accuracy on par with the conventional stereo pipeline, even in highly non-planar terrain.

Novel Assistive Device for Teaching Crawling Skills to Infants

Abstract: Crawling is a fundamental skill linked to development far beyond simple mobility. Infants who have cerebral palsy and similar conditions learn to crawl late, if at all, pushing back other elements of their development. This paper describes the development of a robot (the Self-Initiated Prone Progression Crawler V3, or SIPPC3) that assists infants in learning to crawl. When an infant is placed onboard, the robot senses contact forces generated by the limbs interacting with the ground. The robot then moves or raises the infant’s trunk accordingly. The robot responses are adjustable such that even infants lacking the muscle strength to crawl can initiate movement. The novel idea that this paper presents is the use of a force augmenting motion mechanism to help infants learn how to crawl.

Energy-Aware Terrain Analysis for Mobile Robot Exploration

Abstract: This paper presents an approach to predict energy consumption in mobility systems for wheeled ground robots. The energy autonomy is a critical problem for various battery-powered systems. Specifically, the consumption prediction in mobility systems, which is difficult to obtain due to its complex interactivity, can be used to improve energy efficiency. To address this problem, a self-supervised approach is presented which considers terrain geometry and soil types. Especially, this paper analyzes soil types which affect energy usage models, then proposes a prediction scheme based on terrain type recognition and simple consumption modeling. The developed vibration-based terrain classifier is validated with a field test in diverse volcanic terrain.

Robust Autonomous Flight in Constrained and Visually Degraded Environments

Abstract: This paper addresses the problem of autonomous navigation of a micro aerial vehicle (MAV) inside a constrained shipboard environment for inspection and damage assessment, which might be perilous or inaccessible for humans especially in emergency scenarios. The environment is GPS-denied and visually degraded, containing narrow passageways, doorways and small objects protruding from the wall. This makes existing 2D LIDAR, vision or mechanical bumper-based autonomous navigation solutions fail. To realize autonomous navigation in such challenging environments, we propose a fast and robust state estimation algorithm that fuses estimates from a direct depth odometry method and a Monte Carlo localization algorithm with other sensor information in an EKF framework. Then, an online motion planning algorithm that combines trajectory optimization with receding horizon control framework is proposed for fast obstacle avoidance. All the computations are done in real-time onboard our customized MAV platform. We validate the system by running experiments in different environmental conditions. The results of over 10 runs show that our vehicle robustly navigates 20 m long corridors only 1 m wide and goes through a very narrow doorway (66 cm width, only 4 cm clearance on each side) completely autonomously even when it is completely dark or full of light smoke.

Search and Retrieval of Human Casualties in Outdoor Environments with Unmanned Ground Systems—System Overview and Lessons Learned from ELROB 2014

Abstract: The European Land Robot Trail (ELROB) is a robot competition running for nearly 10 years now. Its focus changes between military and civilian applications every other year. Although the ELROB is now one of the most established competition events in Europe, there have been changes in the tasks over the years. In 2014, for the first time, a search and rescue scenario was provided. This paper addresses this Medical Evacuation (MedEvac) scenario and describes our system design to approach the challenge, especially our innovative control mechanism for the manipulator. Comparing our solution with the other teams’ approaches we will show advantages which, finally, enabled us to achieve the first place in this trial.

The scholarship of teaching & learning in family science

Abstract: This is the introductory article for this special issue on the Scholarship of Teaching and Learning (SoTL) in family science. First, the article presents an overview of SoTL and its intersection with family science along with definition and conceptualization of SoTL. Next, there is explanation of different models for evaluating SoTL scholarship. Third, there is description of a typology of the scholarly questions that can be asked in SoTL. After reviewing these typologies, the article focuses on reviewing SoTL specifically in family science, documenting benefits of engaging in SoTL scholarship, and describing how family scientists are in unique positions to make meaningful contributions to SoTL. The article concludes with concrete recommendations for advancing SoTL in family science.

Admittance Control for Robotic Loading: Underground Field Trials with an LHD

Abstract: In this paper we describe field trials of an admittance-based Autonomous Loading Controller (ALC) applied to a robotic Load-Haul-Dump (LHD) machine at an underground mine near Orebro, Sweden. The ALC was tuned and field tested by using a 14-tonne capacity Atlas Copco ST14 LHD mining machine in piles of fragmented rock, similar to those found in operational mines. Several relationships between the ALC parameters and our performance metrics were discovered through the described field tests. During these tests, the tuned ALC took 61 % less time to load 39 % more payload when compared to a manual operator. The results presented in this paper suggest that the ALC is more consistent than manual operators, and is also robust to uncertainties in the unstructured mine environment.

Experimental Analysis of Receding Horizon Planning Algorithms for Marine Monitoring

Abstract: Autonomous surface vehicles (ASVs) are becoming more widely used in environmental monitoring applications. Due to the limited duration of these vehicles, algorithms need to be developed to save energy and maximize monitoring efficiency. This paper compares receding horizon path planning models for their effectiveness at collecting usable data in an aquatic environment. An adaptive receding horizon approach is used to plan ASV paths to collect data. A problem that often troubles conventional receding horizon algorithms is the path planner becoming trapped at local optima. Our proposed Jumping Horizon (J-Horizon) algorithm planner improves on the conventional receding horizon algorithm by jumping out of local optima. We demonstrate that the J-Horizon algorithm collects data more efficiently than commonly used lawnmower patterns, and we provide a proof-of-concept field implementation on an ASV with a temperature monitoring task in a lake.

Taming the North: Multi-camera Parallel Tracking and Mapping in Snow-Laden Environments

Abstract: Robot deployment in open snow-covered environments poses challenges to existing vision-based localization and mapping methods. Limited field of view and over-exposure in regions where snow is present leads to difficulty identifying and tracking features in the environment. The wide variation in scene depth and relative visual saliency of points on the horizon results in clustered features with poor depth estimates, as well as the failure of typical keyframe selection metrics to produce reliable bundle adjustment results. In this work, we propose the use of and two extensions to Multi-Camera Parallel Tracking and Mapping (MCPTAM) to improve localization performance in snow-laden environments. First, we define a snow segmentation method and snow-specific image filtering to enhance detectability of local features on the snow surface. Then, we define a feature entropy reduction metric for keyframe selection that leads to reduced map sizes while maintaining localization accuracy. Both refinements are demonstrated on a snow-laden outdoor dataset collected with a wide field-of-view, three camera cluster on a ground rover platform.

From ImageNet to Mining: Adapting Visual Object Detection with Minimal Supervision

Abstract: This paper presents visual detection and classification of light vehicles and personnel on a mine site. We capitalise on the rapid advances of ConvNet based object recognition but highlight that a naive black box approach results in a significant number of false positives. In particular, the lack of domain specific training data and the unique landscape in a mine site causes a high rate of errors. We exploit the abundance of background-only images to train a k-means classifier to complement the ConvNet. Furthermore, localisation of objects of interest and a reduction in computation is enabled through region proposals. Our system is tested on over 10 km of real mine site data and we were able to detect both light vehicles and personnel. We show that the introduction of our background model can reduce the false positive rate by an order of magnitude.

Autonomous Greenhouse Gas Sampling Using Multiple Robotic Boats

Abstract: Accurately quantifying total greenhouse gas emissions (e.g. methane) from natural systems such as lakes, reservoirs and wetlands requires the spatial-temporal measurement of both diffusive and ebullitive (bubbling) emissions. Traditional, manual, measurement techniques provide only limited localised assessment of methane flux, often introducing significant errors when extrapolated to the whole-of-system. In this paper, we directly address these current sampling limitations and present a novel multiple robotic boat system configured to measure the spatiotemporal release of methane to atmosphere across inland waterways. The system, consisting of multiple networked Autonomous Surface Vehicles (ASVs) and capable of persistent operation, enables scientists to remotely evaluate the performance of sampling and modelling algorithms for real-world process quantification over extended periods of time. This paper provides an overview of the multi-robot sampling system including the vehicle and gas sampling unit design. Experimental results are shown demonstrating the system’s ability to autonomously navigate and implement an exploratory sampling algorithm to measure methane emissions on two inland reservoirs.

Building, Curating, and Querying Large-Scale Data Repositories for Field Robotics Applications

Abstract: Field robotics applications have some unique and unusual data requirements—the curating, organisation and management of which are often overlooked. An emerging theme is the use of large corpora of spatiotemporally indexed sensor data which must be searched and leveraged both offline and online. Increasingly we build systems that must never stop learning. Every sortie requires swift, intelligent read-access to gigabytes of memories and the ability to augment the totality of stored experiences by writing new memories. This however leads to vast quantities of data which quickly become unmanageable, especially when we want to find what is relevant to our needs. The current paradigm of collecting data for specific purposes and storing them in ad-hoc ways will not scale to meet this challenge. In this paper we present the design and implementation of a data management framework that is capable of dealing with large datasets and provides functionality required by many offline and online robotics applications. We systematically identify the data requirements of these applications and design a relational database that is capable of meeting their demands. We describe and demonstrate how we use the system to manage over 50TB of data collected over a period of 4 years.

The state of the scholarship of teaching and learning in family science

Abstract: This is the introductory article for this special issue on the Scholarship of Teaching and Learning (SoTL) in family science. First, the article presents an overview of SoTL and its intersection with family science along with definition and conceptualization of SoTL. Next, there is explanation of different models for evaluating SoTL scholarship. Third, there is description of a typology of the scholarly questions that can be asked in SoTL. After reviewing these typologies, the article focuses on reviewing SoTL specifically in family science, documenting benefits of engaging in SoTL scholarship, and describing how family scientists are in unique positions to make meaningful contributions to SoTL. The article concludes with concrete recommendations for advancing SoTL in family science.

CoPilot: Autonomous Doorway Detection and Traversal for Electric Powered Wheelchairs

Abstract: In this paper we introduce CoPilot, an active driving aid that enables semi-autonomous, cooperative navigation of an electric powered wheelchair (EPW) for automated doorway detection and traversal. The system has been cleanly integrated into a commercially available EPW, and demonstrated with both joystick and head array interfaces. Leveraging the latest in 3D perception systems, we developed both feature and histogram-based approaches to the doorway detection problem. When coupled with a sample-based planner, success rates for automated doorway traversal approaching 100 % were achieved.

Segmentation and Classification of 3D Urban Point Clouds: Comparison and Combination of Two Approaches

Abstract: Segmentation and classification of 3D urban point clouds is a complex task, making it very difficult for any single method to overcome all the diverse challenges offered. This sometimes requires the combination of several techniques to obtain the desired results for different applications. This work presents and compares two different approaches for segmenting and classifying 3D urban point clouds. In the first approach, detection, segmentation and classification of urban objects from 3D point clouds, converted into elevation images, are performed by using mathematical morphology. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM (Support Vector Machine) with geometrical and contextual features. The second method employs a super-voxel based approach in which the 3D urban point cloud is first segmented into voxels and then converted into super-voxels. These are then clustered together using an efficient link-chain method to form objects. These segmented objects are then classified using local descriptors and geometrical features into basic object classes. Evaluated on a common dataset (real data), both these methods are thoroughly compared on three different levels: detection, segmentation and classification. After analyses, simple strategies are also presented to combine the two methods, exploiting their complementary strengths and weaknesses, to improve the overall segmentation and classification results.

Mobility Assessment of Wheeled Robots Operating on Soft Terrain

Abstract: Optimizing the vehicle mobility is an important goal in the design and operation of wheeled robots intended to perform on soft, unstructured terrain. In the case of vehicles operating on soft soil, mobility is not only a kinematic concept, but it is related to the traction developed at the wheel-ground interface and cannot be separated from terramechanics. Poor mobility may result in the entrapment of the vehicle or limited manoeuvring capabilities. This paper discusses the effect of normal load distribution among the wheels of an exploration rover and proposes strategies to modify this distribution in a convenient way to enhance the vehicle ability to generate traction. The reconfiguration of the suspension and the introduction of actuation on previously passive joints were the strategies explored in this research. The effect of these actions on vehicle mobility was assessed with numerical simulation and sets of experiments, conducted on a six-wheeled rover prototype. Results confirmed that modifying the normal load distribution is a suitable technique to improve the vehicle behaviour in certain manoeuvres such as slope climbing.

Non-Field-of-View Acoustic Target Estimation in Complex Indoor Environment

Abstract: This paper presents a new approach which acoustically localizes a mobile target outside the Field-of-View (FOV), or the Non-Field-of-View (NFOV), of an optical sensor, and its implementation to complex indoor environments. In this approach, microphones are fixed sparsely in the indoor environment of concern. In a prior process, the Interaural Level Difference IID of observations acquired by each set of two microphones is derived for different sound target positions and stored as an acoustic cue. When a new sound is observed in the environment, a joint acoustic observation likelihood is derived by fusing likelihoods computed from the correlation of the IID of the new observation to the stored acoustic cues. The location of the NFOV target is finally estimated within the recursive Bayesian estimation framework. After the experimental parametric studies, the potential of the proposed approach for practical implementation has been demonstrated by the successful tracking of an elderly person needing health care service in a home environment.