Localization In Outdoor Environments Research Articles

Using fuzzy transforms for neural networks-based wireless localization in outdoor environments

As neural network-based localization algorithms are becoming popular, there is a need to shorten the training time and the localization time for sustainability and efficiency purposes. To address such issues, the fuzzy transform (or F-transform for short) is employed here for the first time in a neural network-based localization algorithm. The F-transform is a dimensionality reduction method, which has found several applications over the last decade, but it has not been well explored in the form of a prepending layer to a neural network. In this respect, some properties (including the computational cost) of the F-transformed neural scheme are formally discussed here. The performance of the neural network-based approach with and without F-transform, and with a state-of-the-art reduction technique, i.e. the principal component analysis, is evaluated first on simulated data and then on publicly available real-world data. Different neural network architectures have been tried jointly with the above-mentioned reduction techniques. The numerical experiments show the excellent performance of the proposed fuzzy transform-based approach, which can ensure considerable savings in training time and query response time, without significant losses in accuracy.

On the precision of 6 DoF IMU-LiDAR based localization in GNSS-denied scenarios.

Positioning and navigation represent relevant topics in the field of robotics, due to their multiple applications in real-world scenarios, ranging from autonomous driving to harsh environment exploration. Despite localization in outdoor environments is generally achieved using a Global Navigation Satellite System (GNSS) receiver, global navigation satellite system-denied environments are typical of many situations, especially in indoor settings. Autonomous robots are commonly equipped with multiple sensors, including laser rangefinders, IMUs, and odometers, which can be used for mapping and localization, overcoming the need for global navigation satellite system data. In literature, almost no information can be found on the positioning accuracy and precision of 6 Degrees of Freedom Light Detection and Ranging (LiDAR) localization systems, especially for real-world scenarios. In this paper, we present a short review of state-of-the-art light detection and ranging localization methods in global navigation satellite system-denied environments, highlighting their advantages and disadvantages. Then, we evaluate two state-of-the-art Simultaneous Localization and Mapping (SLAM) systems able to also perform localization, one of which implemented by us. We benchmark these two algorithms on manually collected dataset, with the goal of providing an insight into their attainable precision in real-world scenarios. In particular, we present two experimental campaigns, one indoor and one outdoor, to measure the precision of these algorithms. After creating a map for each of the two environments, using the simultaneous localization and mapping part of the systems, we compute a custom localization error for multiple, different trajectories. Results show that the two algorithms are comparable in terms of precision, having a similar mean translation and rotation errors of about 0.01m and 0.6°, respectively. Nevertheless, the system implemented by us has the advantage of being modular, customizable and able to achieve real-time performance.

Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.

Appearance‐based landmark selection for visual localization

AbstractVisual localization in outdoor environments is subject to varying appearance conditions rendering it difficult to match current camera images against a previously recorded map. Although it is possible to extend the respective maps to allow precise localization across a wide range of differing appearance conditions, these maps quickly grow in size and become impractical to handle on a mobile robotic platform. To address this problem, we present a landmark selection algorithm that exploits appearance co‐observability for efficient visual localization in outdoor environments. Based on the appearance condition inferred from recently observed landmarks, a small fraction of landmarks useful under the current appearance condition is selected and used for localization. This allows to greatly reduce the bandwidth consumption between the mobile platform and a map backend in a shared‐map scenario, and significantly lowers the demands on the computational resources on said mobile platform. We derive a landmark ranking function that exhibits high performance under vastly changing appearance conditions and is agnostic to the distribution of landmarks across the different map sessions. Furthermore, we relate and compare our proposed appearance‐based landmark ranking function to popular ranking schemes from information retrieval, and validate our results on the challenging University of Michigan North Campus long‐term vision and LIDAR data sets (NCLT), including an evaluation of the localization accuracy using ground‐truth poses. In addition to that, we investigate the computational and bandwidth resource demands. Our results show that by selecting 20–30% of landmarks using our proposed approach, a similar localization performance as the baseline strategy using all landmarks is achieved.

Synchronization of Multiple Independent Subarray Antennas: An Application for Angle of Arrival Estimation

In this paper, we present a new algorithm to synchronize multiple individual receiving subarray antennas to form an augmented array antenna system. This augmented array antenna has a large antenna aperture that can be used for angle of arrival (AoA) estimations and it provides a better AoA estimation accuracy than the individual subarray antennas. The proposed algorithm synchronizes the time and the frequency of the different subarray antennas, using a joint maximum likelihood (ML) optimization for a data-aided received signal. The phase coherency between the subarray antennas is achieved by using a combination of the least mean square and ML algorithms. The experimental results and the simulation example show that the proposed algorithm: 1) is stable in estimating the augmented array antenna pattern; 2) provides more accurate AoA estimations than the individual subarray antennas; 3) has no limitation to the structure of the augmented array antenna in space; and 4) converges very fast. Furthermore, the experimental results prove the feasibility of using AoA estimation techniques for Internet of Things localization in outdoor environments.

Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments.

Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks.

Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments.

In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.

Robust and Accurate Monocular Vision-Based Localization in Outdoor Environments of Real-World Robot Challenge

This paper describes our approach to perform robust monocular camera metric localization in the dynamic environments of Tsukuba Challenge 2016. We address two issues related to vision-based navigation. First, we improved the coverage by building a custom vocabulary out of the scene and improving upon place recognition routine which is key for global localization. Second, we established possibility of lifelong localization by using previous year’s map. Experimental results show that localization coverage was higher than 90% for six different data sets taken in different years, while localization average errors were under 0.2 m. Finally, the average of coverage for data sets tested with maps taken in different years was of 75%.

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

Here we report on a new method for robotic gas source localization. Research efforts are being made to develop mobile robots that can autonomously search for gas sources. The tasks envisaged for such robots include detecting gas leaks in industrial plants and exploring gas emissions in landfill sites. If a mobile robot is provided with gas sensors, it can detect the presence of a target gas drifting in the air. However, to find the location of the gas source is not a trivial task for the robot. Gas molecules released in the air are carried by the airflow and form a plume in the downwind direction. Since the airflow the robot encounters in gas source localization applications is almost always turbulent, the plume randomly meanders and comes to have a complicated patchy structure. Gas source localization in outdoor environments is especially challenging owing to the large fluctuations in the wind direction. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. Two different turbulent plume models were compared regarding the accuracy in the estimated gas source location. The results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

LOCALIZATION USING FILTERED DGPS

In this paper, localization of the robot is achieved by considering two Global Positioning Systems (GPS) or DGPS. Differential Global Positioning System (DGPS) is interfaced with MBED with the help of Zigbee protocol. For accurate localization of mobile robot DGPS is preferred. Filters are used to remove the erroneous noise from the data obtained from GPS. Low pass IIR filter for DGPS is realized. The project work discusses each of these approaches for localization in Outdoor environment. The above algorithm is implemented on MBED Platform. Simulation results are extracted using Matlab.

EKF Based GPS/Odometer Data Fusion For Precise Robot Localization In Outdoor Environment

This paper describes a method of data fusion for outdoor autonomous robot equipped with onboard GPS receiver and Odometer using Extended Kaman filter. Both GPS and Odometer having their own advantage and disadvantage for accurate position information of Robot in unknown/outdoor environment. Described Data fusion technique fuse position input of both Odometer and GPS to generate most accurate position estimation with less uncertainty (standard deviation) by using Extended Kalman Filter. Once precise position of Robot obtained in unknown environment then other control actions can be applied on Robot to put it on the right

야지환경에서 연합형 필터 기반의 다중센서 융합을 이용한 무인지상로봇 위치추정

This paper presents UGV localization using multi-sensor fusion based on federated filter in outdoor environments. The conventional GPS/INS integrated system does not guarantee the robustness of localization because GPS is vulnerable to external disturbances. In many environments, however, vision system is very efficient because there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper uses the scene matching and pose estimation based vision navigation, magnetic compass and odometer to cope with the GPS-denied environments. NR-mode federated filter is used for system safety. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

Wireless sensor node localization by multisequence processing

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.

Federated‐filter‐based unmanned ground vehicle localization using 3D range registration with digital elevation model in outdoor environments

AbstractAn integrated GPS/INS does not guarantee localization robustness in outdoor environments, because GPS is vulnerable to external disturbances. However, a digital elevation model (DEM) contains 3D data on the terrain over a specified area and hence can provide in‐depth localization information during GPS blockage. This paper proposes federated‐filter‐based localization using three‐dimensional (3D) range registration with a DEM. A no‐reset‐feedback method is used and a 3D LIDAR sensor, magnetic compass, and odometer are used to correct INS errors in GPS blockage. For 3D range registration with DEM, this paper presents a framework based on the weighted registration scheme of two transformations, pairwise registration and registration with DEM, with the INS position and attitude information. The transformation is first determined by comparing the results of two registration methods with the INS position and is then modified to replace the orientation result of 3D registration with the INS attitude. A multilayered DEM approach using the height of the integrated system is also used to constrain the search range of DEM into three layers near the current unmanned ground vehicle (UGV) position when the corresponding point is searched for in the DEM. Experimental results show that the proposed localization algorithm can greatly enhance the robustness and accuracy of UGV localization in outdoor environments. © 2012 Wiley Periodicals, Inc.

複数の外界センサを用いた位置推定モジュール群による屋外環境における自己位置推定

複数の外界センサを用いた位置推定モジュール群による屋外環境における自己位置推定

Outdoor Localization Using Stereo Vision Under Various Illumination Conditions

We present a mobile robot localization method using only a stereo camera. Vision-based localization in outdoor environments is a challenging issue because of extreme changes in illumination. To cope with varying illumination conditions, we use two-dimensional occupancy grid maps generated from three-dimensional point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. The grid maps are not significantly affected by illumination conditions because occupancy information and salient line segments can be robustly obtained. On the grid maps, a robot's poses are estimated using a particle filter that combines visual odometry and map matching. We use edge-point-based stereo simultaneous localization and mapping to obtain simultaneously occupancy information and robot ego-motion estimation. We tested our method under various illumination and weather conditions, including sunny and rainy days. The experimental results showed the effectiveness and robustness of the proposed method. Our method enables localization under extremely poor illumination conditions, which are challenging for even existing state-of-the-art methods.

Localization using GPS and VISION aided INS with an image database and a network of a ground-based reference station in outdoor environments

GPS/INS integrated systems do not guarantee robustness and accuracy of localization, because GPS has vulnerability to external disturbances. However, the overall performance and reliability of the system can be significantly improved by fusing multiple sensors with a different operating principle. In outdoor environments where GPS may be blocked, there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper proposes an improved localization algorithm based on the hierarchical federation of three measurement layers, i.e., GPS, INS, and visual localization, to overcome the shortcomings of GPS/INS integrated systems. The proposed algorithm automatically switches the operation modes according to GPS status and a network of a ground-based reference station. A vocabulary tree with SURF is used in the visual localization method. In the data fusion of visual localization and INS, an asynchronous and time-delayed data fusion algorithm is presented because visual localization is always time-delayed compared with INS. By using DGPS to obtain the reference position under the dynamic conditions of the reference station, the restrictions of the conventional DGPS are overcome and all UGVs within WiBro communication range of the reference station can accurately estimate the position with a common GPS. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.

Mobile Robot Localization in Outdoor Environments Based on Near-infrared Vision

A self-localization system based on near-infrared vision and bar-coded landmarks for robot navigating in out-door environment with variable light conditions and electromagnetic interference is presented.The near-infrared illuminator and omni-directional vision are used for recognizing bar-coded landmarks.Data from vision system and odometry are fused with an extended Kalman filter(EKF) to realize robot self-localization.The experiment result demonstrates that the proposed method eliminates the effect of light variations on robot localization in outdoor long-range navigation.

Elevation moment of inertia: A new feature for Monte Carlo localization in outdoor environment with elevation map

AbstractThe elevation map is one of the most popular maps for outdoor navigation. We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization. © 2010 Wiley Periodicals, Inc.

SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments

In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straightforward method that relies on local image features to compare single-image pairs. We first look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The final localization rate in the single-image matching, cross-seasonal case is between 80% to 95%.