formation during the fully autonomous test, Test 16. This test consisted of the agent being commanded to go from point A, denoted in Figure 5.7, to point B, then from point B to C, and finally from point C to point D. In between each step, the agent was stopped, and the odometry was reset, resulting in the large jagged sections seen near each point. While the agent was traveling, it did not travel in perfectly straight lines or remain centered in the hallway as there were obstacles along the walls of the hallways that caused the agent to shift to avoid them. This visualization further shows that the odometry information is very unreliable as it often indicates travel into invalid areas of the map or even outside the map. While the odometry is generally incorrect globally, the local changes and turns are correct, with the exception of the big changes where the odometry was reset. In terms of the algorithm, this visualiza- tion shows good and bad performance. In some cases, such as along the top of the map, the estimated path follows local changes from the odometry while also correctly identifying the global position. In other cases, such as on the right side and after any odometry reset, it is shown that the algorithm follows the changes in the odometry too closely, causing errors. This happens as a result of updating the particles with local movement from the odometry each iteration.
visual odometry (EVO) , magnetic localization approach proposed in Son et al. (2017) and with the same CNN- RNN system in Fig. 3 except we disabled the magnetic localizationfusion path. We call this last configuration deep visual odometry (DVO). The average translational and rotational RMSEs for deepsensorfusion, EVO, mag- netic localization and DVO against different path lengths are shown in Figs. 8 and 9, respectively.The results indi- cate that deepsensorfusion clearly outperforms all other configurations, while magnetic localization outperforms EVO and DVO. Some qualitative tracking results and corresponding ground truth trajectories for deepfusion approach, DVO and magnetic localization are demon- strated in Fig. 7 for visual reference. As seen in sam- ple trajectories, deepfusion is able to stay close to the ground-truth pose values for even complex, fast rotational and translational motions, where both EVO and mag- netic localization by themselves clearly deviate from the ground-truth trajectory. Thus, we can conclude that deepfusion makes effective use of both sensor data streams Based on our evaluations, we presume that the hybrid use of the CNN-LSTM architecture enabled learning from both magnetic and visual information effectively which led to optimal localization results. We run our training and testing sessions both on an Amazon EC2 p2.xlarge machine instance. The duration of the training session was 8 h and 37 min. The 6-DoF pose estimation per image pair and magnetic data took 35 ms Figs. 8 and 9.
For existing indoorlocalization algorithm has low accuracy, high cost in dep- loyment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensorfusion. The pedestrian’s localization in indoor environment is described as dynamic sys- tem state estimation problem. The algorithm combines the smart mobile ter- minal with indoorlocalization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization in- formation have been used to improve the filtering precision and the stability.
As far as the first subject is concerned, a robot should be able to localize itself in a given reference frame. This problem is studied in detail to achieve a proper and affordable technique for self-localization, regardless of specific environmental features. The proposed solution relies on the integration of relative and absolute position measurements. The former are based on odometry and on an inertial measurement unit. The absolute posi- tion and heading data instead are measured sporadically anytime some landmark spread in the environment is detected. Due to the event-based nature of such measurement data, the robot can work autonomously most of time, even if accuracy degrades. Of course, in order to keep positioning uncertainty bounded, it is important that absolute and relative position data are fused properly. For this reason, four different techniques are analyzed and compared in the dissertation.
INDEX TERMS State estimation, extended Kalman filter, indoorlocalization, sensorfusion, laser localiza- tion, visual localization, RFID localization, social robotics.
The presence of people, sculptures, counters, and paintings, among other factors mean that museums and trade fairs are very complex environments. Robot localization is therefore essential for any high-level task that it might perform in that kind of environment. Many studies have been aimed at achieving dependable indoorlocalization of robots. The study presented by  implemented a sensorfusion with a sonar sensor and a Microsoft Kinect for acquiring depth information. Another, in , described a robust Multi-sensorfusionlocalization for indoor mobile robots. It was based on a set-membership estimator, developed by fusing a laser scanner and an odometer, using a point-to-line iterative clos- est point approach, to match successive environmental data
At communication protocol level, distributed algo- rithms such as data aggregation, fusion, and coop- erative localization are made possible. Application- level issues include, for example, monitoring and user interaction. A location node may integrate sensors with which it can monitor its environment and/or the object or person it is attached to. Furthermore, a person with a location node may send predefined messages and read status information by using a simple user interface (UI) provided by the loca- tion node or a device connected to the location node. Applications requiring reliable communication should be taken into account when designing the data transfer support.
These elements can have either positive or negative impact in the received signal; increasing or decreasing the RSSI measured value. For example without diffraction, scattering and reflection the signal cannot reach some places in indoor environments.
Previous research in wireless positioning show that Wi-Fi-enabled devices can be located by applying one of two types of location-sensing techniques, propagation based  and location fingerprinting . Propagation-based techniques measure the received signal strength (RSS), angle of arrival (AOA), or time difference of arrival (TDOA) of received signals and apply mathematical models to determine the location of the device. The drawbacks of propagation- based method are the need to compute every condition that can cause wave signal to blend in order to achieve accurate localization.
The Bayesian filters introduced in Section 3 were used to track and locate the robot of the experimental setup in Section 2. Recall that a robot was moved along a tra- jectory while emitting an UWB ranging signal; such sig- nal was received by a set of UWB sensors located in an office environment with known locations. The problem tackled in this article is that of a data fusion center in charge of tracking the position of the robot accounting for the measured ranges. Initial position ambiguity was modeled with a Gaussian random variable with covar- iance 10 · I 2
Figure 1.1: The diagram of the dual-sensor localisation system. An improved approach based on WLAN data only is used when localising a user between pre-selected locations
or movement of personnel within a building. In these environments, the radio properties are highly dynamic, and a radio map captured at a certain point in time cannot be used reliably for localization without accounting for these dynamic changes [62,38,114,152]. Interference and noise are often-mentioned challenges [48,135]. Localisation technologies based on RF technologies can be attractive due to the ubiquity of certain infrastructural technologies, such as wireless data networks, that may already be present in the facilities. Care must be taken, however, in evaluating the impact of the physical environment on the RF lo- calization technologies, as the solution may be rendered non-operative in certain clinical settings. For image-based localisation challenges exist in case of significant occlusion or lightning changes. Also most of image-based localisation methods cannot easily distinguish very similar locations thus introducing localisation errors in case of locations physically far from each other.
The BP algorithm has been widely used as a supervised learning algorithm in feed-forward multilayer ANN based on the Gradient Descent method. That attempts to minimize the error of the network by moving down the gradient of the error curve as stated. However, the BP has a slow con- vergence. Consequently, many faster algorithms were pro- posed to speed up the convergence of the BP and can be grouped in two main categories : (i) uses heuristic techniques developed from an analysis of the performance of the standard steepest descent algorithm, for instance BPM, to prevent instabilities caused by a too-high learning rate; and (ii) uses standard numerical optimization tech- niques, such as LM algorithm, which is an approximation to Newton’s method, suitable for training small and me- dium-sized problems.
final map would store both the average infrared reflectivity observed at the partic- ular location as well as the variance of those values very similar to a Kalman filter. Like , the 2-dimensional histogram filter would comprise of the motion update to reduce confidence in the estimate based on motion, and the measurement update, to increase confidence in the estimate based on sensor data. Since the motion update was theoretically quadratic in the number of cells, in practice considering only the neighboring cells would be perfectly accessible. The motion update would be followed by the measurement update, in which the incoming laser scans would be used to refine the vehicle’s velocity. The results were quite impressive during a ten-minute drive, a RMS lateral correction of 66cm was necessary, and the localizer was able to correct large errors of upto 1.5 meters. The resulting error after localization was extremely low, with a RMS value of 9cm. The autonomous car was able to drive in densely populated urban environments like the 11th Avenue in downtown Manhattan which would be impossible before without such high precision in localization. In conclu- sion, this method was able to improve the precision of the best GPS/IMU systems available by an order of magnitude, both vertically and horizontally, thus enabling decimeter-level accuracy which is more than sufficient for autonomous driving.
the localization accuracy with sensors like an IMU using a technique called sensorfusion. Another technique employed uses lowlevel RF data like the impulse or phase response in Ultrawideband (UWB) localization systems to determine whether a measurement is LOS or NLOS. This requires actually having access to that information on a hardware level in the first place which increases the cost and complexity of the localization system. Most solutions also employ the use of a Kalman filter (KF) for fusing data or correcting noise. While typically accurate, these methods are as a result very applicationspecific and do not generalize well. Lastly, since these are rangebased techniques another solution is to simply add a lot more fixed anchors to the system so that there are never any NLOS conditions – this solution greatly increases system costs and still can’t guarantee LOS conditions in a dynamic environment.
† Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Abstract—Indoor navigation using the existing wireless in- frastructure and mobile devices is a very active research area.
The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algorithm is developed to infer mobile location based on RSSI measurements. The simulation results show that the proposed algorithm significantly outperforms the extended Kalman filter (EKF), and achieves competitive location accuracy compared with the round trip time (RTT) based ultra-wideband (UWB) system.
DRL network structure of model fusion
Using the historical empirical data store in different structures by the experience playback mechanism to update the power values of the network, we call it the neural network of the model fusion. When the neural network is trained, the hypothesis is independent and distributed. There is a correlation between the data collected through intensive learning, and the use of these data for sequential training, and the neural network is unstable. Experience playback can break the correlation between data. The aim of this method is to improve the stability of the training process and accelerate the convergence rate .Specifically, agents still use -greedy strategies to play games on the test set, which can lead to this uncertainty. Specifically, agents still use strategies to play games on the test set, which can lead to this uncertainty. We hope that the agent collects as much training data as possible for training by sampling from a large number of historical samples and using the empirical data of multiple different DNN(deep neural network) architectures. When the data in the sampling is replayed, different sampling methods are used and the sample is sampled as far as possible. For example, the two samples are more than 4 frames apart, and the frame that does not sample is in the terminal state, and the frame that is in the terminal state has no subsequent frame. Different network structures also indirectly increase historical samples. Different DRL (deep reinforcement learning) uses different convolutional neural network architecture:
ABSTRACT With the progressive increase of stress, anxiety and depression in working and living environ- ment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires.
However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deeplearning architecture within the device for multi-feature classification and fusion analysis. This enables the deeplearning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deeplearning architecture can effectively classify mental state with better performance.
Figure 6 shows the fusion combinations and popular approaches in sensor-driven indoor localisation in the last decade. This particular figure is not exhaustive, and as it was noted before, is only attached as a starting point for further investigation of a particular fusion combination. Indeed, there is an evident community preference towards sensors which, either have a broad foundation on which to build the algorithms such as RF, or are based on modalities which are easy to come by, such as IMUs and magnetometers. While magnetometers have seen extensive use as part of PDR applications where they usually establish direction, there is lack of recent, comprehensive study of its viability with RF sensors. Both types utilise fingerprinting as part of its training phase. This type of data could be collected simultaneously, and can often reuse already existing IMU chipsets reported in various studies.
Abstract. Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is difficult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to sup- press this limitation and to provide location everywhere (even where a structured environment doesn’t exist) a wearable inertial navigation sys- tem is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.
The reasons for choosing velocity and the function of the camera have been previously addressed. Herein we discuss the expected performance of the camera and what the actual data said about the performance of the camera.
The camera was constantly trying to identify objects in it’s field of view. The door locks of interest were 2in square bounded objects. Object scaling was managed by the camera model (section 3.2). Given the locks were a known size the distance to the object could be approximated and as such the change in distance could be computed. The Haar classifier would detect and display the bounding square around any detected objects. Due to noise in the sensor two identical pictures of the object are not numerically identical, while they may be semantically identical. These slight variations mostly come from small lighting differences between captured frames. Due to these variations it was expected that during stationary operation the Haar classifier would ’wobble’. This wobble is where the classifier detects the door lock bounds in slightly different pixel locations during stationary operation. It is considered part of the noise in the camera sensor. It is this wobble that shows up as error in the velocity measurements.
tactile, force-torque, vision, laser, sonar, proprioception, ac- celerometers, etc. An important problem and research topic in robotics is the question of how to fuse multiple sensory modalities to provide the robot with advanced perception capabilities. However, in the context of contact localization in grasping and manipulation tasks, the fusion of multiple sensory information has not been addressed so far . We propose a sensory information fusion approach for contact detection and localization. The approach relies on the generation of contact hypotheses and the fusion of these hypotheses to determine the likelihood of a contact at a certain location leading to an improved robustness and precision of contact detection. In addition, the approach allows the integration of multiple sensors, environment, context and predictions. We have implemented the proposed approach on two dual-arm robots and validated it through several experiments.
Weakly-supervised object localization: There have been a number of recent works exploring weakly- supervised object localization using CNNs [1, 16, 2, 15]. Bergamo et al  propose a technique for self-taught object localization involving masking out image regions to iden- tify the regions causing the maximal activations in order to localize objects. Cinbis et al  combine multiple-instance learning with CNN features to localize objects. Oquab et al  propose a method for transferring mid-level image representations and show that some object localization can be achieved by evaluating the output of CNNs on multi- ple overlapping patches. However, the authors do not ac- tually evaluate the localization ability. On the other hand, while these approaches yield promising results, they are not trained end-to-end and require multiple forward passes of a network to localize objects, making them difficult to scale to real-world datasets. Our approach is trained end-to-end and can localize objects in a single forward pass.