Biases in object location estimation: the role of camera rotations and translation

The current study investigated a systematic bias in spatial memory in which people, following a perspective shift from encoding to recall, indicated the location of an object further to the direction of the shit. In Experiment 1, we documented this bias by asking participants to encode the position of an object in a virtual room and then indicate it from memory following a perspective shift induced by camera translation and rotation. In Experiment 2, we decoupled the influence of camera translations and camera rotations and examined also whether adding more information in the scene would reduce the bias. We also investigated the presence of age-related differences in the precision of object location estimates and the tendency to display the bias related to perspective shift. Overall, our results showed that camera translations led to greater systematic bias than camera rotations. Furthermore, the use of additional spatial information improved the precision with which object locations were estimated and reduced the bias associated with camera translation. Finally, we found that although older adults were as precise as younger participants when estimating object locations, they benefited less from additional spatial information and their responses were more biased in the direction of camera translations. We propose that accurate representation of camera translations requires more demanding mental computations than camera rotations, leading to greater uncertainty about the position of an object in memory. This uncertainty causes people to rely on an egocentric anchor thereby giving rise to the systematic bias in the direction of camera translation.

Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System

Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.

Improved Energy and Communication for Underwater Wireless Sensor Network Based Location Estimation

The area of underwater wireless sensor networks (UWSNs) is garnering an increasing attention from researchers due to its broad potential for exploring and harnessing oceanic sources of interest. Because of the need for real-time remote data monitoring, underwater acoustic sensor networks (UASNs) have become a popular choice. The restricted availability and nonrechargeability of energy resources, as well as the relative inaccessibility of deployed sensor nodes for energy replenishment, forced the development of many energy optimization approaches un the UASN. Clustering is an example of a technology that improves system scalability while also lowering energy consumption. Due to the unstable underwater environment, coverage and connectivity are two important features that determine the proper detection and communication of events of interest in UWSN. A sensor network consists of several nodes that are low in cost and have a battery with low capacity. In wireless sensor networks, knowing the position of a specific device in the network is a critical challenge. Many wireless systems require location information from mobile nodes. Keywords: MAC, Communication cost, IDV-Hop algorithm, Localization, Ranging error, unconstrained optimization, Wireless sensor network, Distributed Least Square

DEVELOPMENT OF LOCATION ESTIMATION ALGORITHM UTILIZING RSSI FOR LORA POSITIONING SYSTEM

LoRa is identified as Long-Range low power network technology for Low Power Wide Area Network (LPWAN) usage. Nowadays, Global Positioning System (GPS) is an important system which is used for location and navigation predominantly used in outdoor but less accurate in indoor environment. Most of LoRa technology have been used on the internet-of-things (ioT) but very few use it as localization system. In this project, a GPS-less solution is proposed where LoRa Positioning System was developed which consists of LoRa transmitter, LoRa transceiver and LoRa receiver. The system has been developed by collecting the RSSI which is then used for the distance estimation. Next, Kalman filter with certain model has been implemented to overcome the effect of multipath fading especially for indoor environment and the trilateration technique is applied to estimate the location of the user. Both distribution estimation results for Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) condition were analyzed. Then, the comparison RMSE achievement is analyzed between the trilateration and with the Kalman Filter. GPS position also were collected as comparison to the LoRa based positioning. Lastly, the Cumulative Density Function (CDF) shows 90% of the localization algorithm error for LOS is lower than 0.82 meters while for NLOS is 1.17 meters.

Localization Algorithm for 3D Sensor Networks: A Recursive Data Fusion Approach

Location-based applications for security and assisted living, such as human location tracking, pet tracking and others, have increased considerably in the last few years, enabled by the fast growth of sensor networks. Sensor location information is essential for several network protocols and applications such as routing and energy harvesting, among others. Therefore, there is a need for developing new alternative localization algorithms suitable for rough, changing environments. In this paper, we formulate the Recursive Localization (RL) algorithm, based on the recursive coordinate data fusion using at least three anchor nodes (ANs), combined with a multiplane location estimation, suitable for 3D ad hoc environments. The novelty of the proposed algorithm is the recursive fusion technique to obtain a reliable location estimation of a node by combining noisy information from several nodes. The feasibility of the RL algorithm under several network environments was examined through analytic formulation and simulation processes. The proposed algorithm improved the location accuracy for all the scenarios analyzed. Comparing with other 3D range-based positioning algorithms, we observe that the proposed RL algorithm presents several advantages, such as a smaller number of required ANs and a better position accuracy for the worst cases analyzed. On the other hand, compared to other 3D range-free positioning algorithms, we can see an improvement by around 15.6% in terms of positioning accuracy.

A study on recognizing multi-real world object and estimating 3D position in augmented reality

As augmented reality technologies develop, real-time interactions between objects present in the real world and virtual space are required. Generally, recognition and location estimation in augmented reality are carried out using tracking techniques, typically markers. However, using markers creates spatial constraints in simultaneous tracking of space and objects. Therefore, we propose a system that enables camera tracking in the real world and visualizes virtual visual information through the recognition and positioning of objects. We scanned the space using an RGB-D camera. A three-dimensional (3D) dense point cloud map is created using point clouds generated through video images. Among the generated point cloud information, objects are detected and retrieved based on the pre-learned data. Finally, using the predicted pose of the detected objects, other information may be augmented. Our system estimates object recognition and 3D pose based on simple camera information, enabling the viewing of virtual visual information based on object location.