Jingbin Liu

A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.

Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.

Human Behavior Cognition Using Smartphone Sensors

This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.

Inquiry-Based Bluetooth Indoor Positioning via RSSI Probability Distributions

This paper presents an inquiry-based Bluetooth indoor positioning solution via RSSI probability distributions. A practical system architecture is designed after the Bluetooth protocol and profiles are studied. Weibull function is applied for approximating the Bluetooth signal strength distribution in the data training phase. The Histogram Maximum Likelihood position estimation based on Bayesian theory is utilized in the location determination phase. The results show the possibility of indoor positioning through inquiring the Bluetooth-enabled handsets in range. Weibull distribution improves the performance of fingerprinting. The practicality of the system architecture is also proved by the outcome of a test campaign.

The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point cloud, and the diameter-at-breast-height (DBH) of each tree was estimated. The detected-results were compared with field measurements and with those derived from the single-scan terrestrial laser scanning (TLS) data. The experiment showed that the mapping accuracy was 88% and the root mean squared error of DBH estimates of individual trees was 2.39 cm, which is acceptable for practical applications and was similar to the results achieved using TLS. The main advantages of the image-based point cloud data lie in the low cost of the equipment required for the data collection, the simple and fast field measurements and the automated data processing, which may be interesting and important for certain applications, such as field inventories by landowners who do not have supports from external experts. The disadvantages of the image-based point cloud data include the limited capability of mapping small trees and complex forest stands.

iParking: An Intelligent Indoor Location-Based Smartphone Parking Service

Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution.

Forest Data Collection Using Terrestrial Image-Based Point Clouds From a Handheld Camera Compared to Terrestrial and Personal Laser Scanning

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Accelerometer assisted robust wireless signal positioning based on a hidden Markov model

Reliable and accurate indoor positioning remains nowadays as one of the greatest challenges in the area of personal navigation and location based services (LBS). This manuscript proposes methods to improve the accuracy and robustness of indoor positioning using signal strength measurements of Wireless Local Area Networks (WLAN), and presents three aspects of contributions. First, the Weibull function is employed to represent the distribution of the signal strength over time. Thus, the impact of the signal strength variation on the fingerprinting database is mitigated, and fewer samples are required for training the database. Second, the accelerometer sensor is utilized to provide the pedestrian dynamics information, which is used to improve the positioning accuracy and reliability. Lastly, hidden Markov model (HMM) based particle filters are performed to compute the positioning solution through combining the signal strength measurements with the pedestrian dynamics information. Through the experimental evaluation of three scenarios, the proposed methods were found to improve significantly the accuracy and robustness of WLAN positioning. Due to their affordable computational load, the positioning methods proposed can be implemented for indoor navigation on mass-market mobile devices without any extra cost requirements.

International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.

Fast fingerprint database maintenance for indoor positioning based on UGV SLAM.

Indoor positioning technology has become more and more important in the last two decades. Utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of OPportunity (SOP) is a promising alternative navigation solution. However, as the RSSIs vary during operation due to their physical nature and are easily affected by the environmental change, one challenge of the indoor fingerprinting method is maintaining the RSSI fingerprint database in a timely and effective manner. In this paper, a solution for rapidly updating the fingerprint database is presented, based on a self-developed Unmanned Ground Vehicles (UGV) platform NAVIS. Several SOP sensors were installed on NAVIS for collecting indoor fingerprint information, including a digital compass collecting magnetic field intensity, a light sensor collecting light intensity, and a smartphone which collects the access point number and RSSIs of the pre-installed WiFi network. The NAVIS platform generates a map of the indoor environment and collects the SOPs during processing of the mapping, and then the SOP fingerprint database is interpolated and updated in real time. Field tests were carried out to evaluate the effectiveness and efficiency of the proposed method. The results showed that the fingerprint databases can be quickly created and updated with a higher sampling frequency (5Hz) and denser reference points compared with traditional methods, and the indoor map can be generated without prior information. Moreover, environmental changes could also be detected quickly for fingerprint indoor positioning.