2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)最新文献

{"title":"Perception-aided Visual-Inertial Integrated Positioning in Dynamic Urban Areas","authors":"X. Bai, Bo Zhang, W. Wen, L. Hsu, Huiyun Li","doi":"10.1109/PLANS46316.2020.9109963","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109963","url":null,"abstract":"Visual-inertial navigation systems (VINS) have been extensively studied in the past decades to provide positioning services for autonomous systems, such as autonomous driving vehicles (ADV) and unmanned aerial vehicles (UAV). Decent performance can be obtained by VINS in indoor scenarios with stable illumination and texture information. Unfortunately, applying the VINS in dynamic urban areas is still a challenging problem, due to the excessive dynamic objects which can significantly degrade the performance of VINS. Detecting and removing the features inside an image using the deep neural network (DNN) that belongs to unexpected objects, such as moving vehicles and pedestrians, is a straightforward idea to mitigate the impacts of dynamic objects on VINS. However, excessive exclusion of features can significantly distort the geometry distribution of visual features. Even worse, excessive removal can cause the unobservability of the system states. Instead of directly excluding the features that possibly belong to dynamic objects, this paper proposes to remodel the uncertainty of dynamic features. Then both the healthy and dynamic features are applied in the VINS. The experiment in a typical urban canyon is conducted to validate the performance of the proposed method. The result shows that the proposed method can effectively mitigate the impacts of the dynamic objects and improved accuracy is obtained.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131724552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improve GNSS Orbit Determination by using Estimated Tropospheric and Ionospheric Models","authors":"C. Bryan, Maisonobe Luc","doi":"10.1109/PLANS46316.2020.9110152","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110152","url":null,"abstract":"Orbit Determination is a technique used to estimate the position of a satellite from its observable measurements. Missing or incorrect modeling of troposphere and ionosphere delays is one of the major error source in space geodetic techniques such as Global Navigation Satellite Systems (GNSS). Accurate computation of these two delays is a mandatory step to cope with accuracy needs which are close to centimeter or millimeter levels. This paper presents the different steps of development of estimated tropospheric and ionospheric models. All these models are included in the Orekit open-source space flight dynamics library. Adding estimated tropospheric and ionospheric models into an orbit determination process can be a difficult procedure. Computing and validating measurement derivatives with respect to troposphere and ionosphere parameters are critical steps. To cope with this constraint, we used the Automatic Differentiation technique to avoid the calculation of the derivatives of long equations. Automatic Differentiation is equivalent to calculating the derivatives by applying chain rule without expressing the analytical formulas. Therefore, Automatic Differentiation allows a simpler computation of the derivatives and a simpler validation. This paper presents how the Jacobian measurement matrix is computed by Automatic Differentiation. It also describes the impact of using estimated tropospheric and ionospheric models. Finally, a study of different model configurations is performed in order to highlight the relevant tropospheric and ionospheric parameters to estimate. The performance of the different models is demonstrated under GPS orbit determination conditions. Both satellite state vector estimation and measurement residuals quality are used as indicator to quantify the orbit determination performance. This paper addresses that estimated tropospheric and ionospheric models are actually more accurate than empirical models to estimate satellite state vector in GNSS orbit determination. A gain of about 60% is obtained on the estimation of the satellite position when estimated models are used, without altering the computation time.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real Time Results of Vector Delay Lock Loop in a Light Urban Scenario","authors":"Katrin Dietmayer, F. Kunzi, F. Garzia, M. Overbeck, W. Felber","doi":"10.1109/PLANS46316.2020.9109832","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109832","url":null,"abstract":"The need of a robust navigation solution increased during the last decade. As an example, for autonomous driving it is essential to have a stable satellite signal tracking even in difficult environments such as urban canyons. The paper describes a real-time vector delay lock loop (VDLL) approach implemented directly on the GNSS receiver hardware to achieve a deep integration in the signal processing. This approach uses the code correlation values fetched directly from the receiver hardware to provide an estimated position, velocity and time (PVT) solution using an extended Kalman filter (EKF). The solution is then used to calculate new steering code values for all channels at the same time, which are sent back to the hardware to steer the code numerically controlled oscillators (NCOs). The approach is tested in a real-world light urban scenario, where different signal degradations occur including multipath, signal shadowing or reflections. The VDLL real-time implementation is analyzed and compared to a reference system. A detailed description of the scenario and the used GNSS receiver hardware is shown.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132940255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GNSS Interference Source Tracking using Kalman Filters","authors":"Sanat K. Biswas, E. Çetin","doi":"10.1109/PLANS46316.2020.9109997","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109997","url":null,"abstract":"Modern infrastructure and a myriad of services rely on positioning and timing information provided by Global Navigation Satellite Systems (GNSS) and in particular the Global Positioning System (GPS). However, given their low received signal power levels, GNSS signals are vulnerable to Radio Frequency Interference (RFI), either from non-intentional or intentional (jamming), sources. Hence, GNSS itself has become a critical infrastructure which must be protected. Since RFI source is unknown a priori, passive localization systems consisting of spatially distributed Sensor Nodes (SNs) are needed to geo-locate the RFI. These systems typically use source Angle of Arrival (AOA), Time Difference of Arrival (TDOA) or a combination of AOA/TDOA measurements which are non-linear in nature, to estimate the RFI position. Also, dynamics associated with the RFI source(s) further complicates the geo-localization process. This paper explores and reports on the use of various Kalman Filters in combining AOA and TDOA measurements for efficient geo-localization and tracking of dynamic and stationary RFI sources based on real measurements from one such geo-localization system. We report on and contrast the geo-localization accuracies and computational complexities of the Extended, Unscented and Single Propagation Unscented Kalman Filters along with the traditional snap-shot approach.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133015526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperative swarm localization and mapping with inter-agent ranging","authors":"Young-Hee Lee, Chen Zhu, G. Giorgi, C. Günther","doi":"10.1109/PLANS46316.2020.9110227","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110227","url":null,"abstract":"Compared to a single robot, a swarm system can conduct a given task in a shorter time, and it is more robust to system failures of each agent. To successfully execute cooperative missions with multiple agents, accurate relative positioning is important. If global positioning (e.g. with a GNSS-based positioning) is available, we can easily compute relative positions. In environments where a global positioning system is unreliable or unavailable, visual odometry can be applied for estimating each agent's egomotion, by exploiting onboard cameras. Using these self-localization results, relative positions between agents can be estimated, once the relative geometry between agents is initialized. However, since visual odometry is a dead-reckoning process, the estimation errors accumulate inherently without bounds. We propose a cooperative localization method using visual odometry and inter-agent range measurements. Using the proposed method, we can reduce the drifts in position estimates with very modest requirements on the communication channel between agents.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Modal Localization: Using automotive radar for absolute geolocation within a map produced with visible-light imagery","authors":"Peter A. Iannucci, Lakshay Narula, T. Humphreys","doi":"10.1109/PLANS46316.2020.9110143","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110143","url":null,"abstract":"This paper explores the possibility of localizing an automotive-radar-equipped vehicle within an urban environment relative to an existing map of the environment created using data from visible light cameras. Such cross-modal localization would enable robust, low-cost absolute localization in poor weather conditions based only on radar even when the vehicle has never previously visited the area. This is because a pre-existing absolutely-referenced visible-light-based map (e.g., constructed from Google Street View images) could be exploited for localization provided that a correspondence between features in this map and the vehicle's radar returns can be established. The greatest challenge presented by cross-modal localization with automotive radar is the extreme sparseness of automotive-radar-produced features, which prevents application of standard computer vision techniques for the cross-modal registration. To the best of the authors' knowledge, cross-modal localization using automotive-grade radar within a visible-light-based map is unprecedented. The current paper demonstrates that it can be used for vehicle localization with horizontal errors below 61 cm (95%).","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134636568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating GNSS Navigation Availability in 3-D Mapped Urban Environments","authors":"Kana Nagai, Titilayo Fasoro, M. Spenko, R. Henderson, B. Pervan","doi":"10.1109/PLANS46316.2020.9109929","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109929","url":null,"abstract":"This research project aims to achieve a future urban environment where people and self-driving cars coexist together while guaranteeing safety. To modify the environment, our first approach is to understand the limitations of GPS/GNSS positioning in an urban area where signal blockages and reflections make positioning difficult. For the evaluation process, we assume reasonable integrity requirements and calculate navigation availability along a sample Chicago urban corridor (State Street). We reject all non-line-of-sight (NLOS) that are blocked and reflected using a 3-D map. The availability of GPS-only positioning is determined to be less than 10% at most locations. Using four full GNSS constellations, availability improves significantly but is still lower than 80 % at certain points. The results establish the need for integration with other navigation sensors, such as inertial navigation systems (INS) and Lidar, to ensure integrity. The analysis methods introduced will form the basis to determine performance requirements for these additional sensors.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131855682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Data Association Method Using Kalman Filter Innovation Vector Projections","authors":"M. Joerger, A. Hassani","doi":"10.1109/PLANS46316.2020.9110229","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110229","url":null,"abstract":"This paper describes the derivation, analysis and implementation of a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. Data association (DA) is the process of assigning currently-sensed features with ones that were previously observed. Most DA methods use a nearest-neighbor criterion based on the normalized innovation squared (NIS). They require complex algorithms to evaluate the risk of incorrect association because sensor state prediction, prior observations, and current measurements are uncertain. In contrast, in this work, we derive a new DA criterion using projections of the extended Kalman filter's innovation vector. The paper shows that innovation projections (IP) are signed quantities that not only capture the impact of an incorrect association in terms of its magnitude, but also of its direction. The IP-based DA criterion also leverages the fact that incorrect associations are known and well-defined fault modes. Thus, as compared to NIS, IPs provide a much tighter bound on the predicted risk of incorrect association. We analyze and evaluate the new IP method using simulated and experimental data for autonomous inertial-aided LiDAR localization in a structured lab environment.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Navigation in Urban Environments Based on an H-infinity Filter and Reinforcement Learning Algorithms","authors":"Ivan Smolyakov, R. Langley","doi":"10.1109/PLANS46316.2020.9109948","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9109948","url":null,"abstract":"In urban areas, robustness of a positioning solution suffers from relatively unpredictable reception of attenuated, non-line-of-sight and multipath-contaminated signals. To reflect a GNSS signal propagation environment, parameters of a state estimation filter need to be adjusted on-the-fly. A mixed H2/ H∞ filter has been considered here to address the vulnerability of a minimum error variance estimator to measurement outliers. An emphasis between the H2filter and the H∞ filter (minimizing the worst-case error) is continuously adjusted by a reinforcement learning (RL) model. Specifically, a continuous action actor-critic RL model with eligibility traces is implemented. The Cramér-Rao lower bound is considered for the filter performance evaluation allowing for the RL reward computation. The algorithm has been tested on a real-world dataset collected with mass-market hardware applying tightly-coupled IMU/GPS sensor integration. A positive RL model learning trend has been identified in two segments of the trajectory with the highest obstruction environment, suggesting the applicability potential of the technique.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115193663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Collaborative Obstacle Avoidance using Small UAS in Indoor Environments","authors":"Daniel Duran, Matt Johnson, R. Stansbury","doi":"10.1109/PLANS46316.2020.9110141","DOIUrl":"https://doi.org/10.1109/PLANS46316.2020.9110141","url":null,"abstract":"This paper proposes an intuitive and collaborative human-robot approach to perception and navigation for sUAS operating in unknown indoor environments, such as disaster response scenarios. The complexity of indoor environments coupled with the urgency of first responder missions make most available obstacle avoidance solutions too complex, unpredictable, impractical or ineffective. This paper presents a human-robot collaborative obstacle avoidance approach with real-time performance. Multiple RGB-D cameras are combined with a front-facing stereo pair to achieve robust localization and 3D perception around the aircraft. Obstacle avoidance is accomplished by dynamically prioritizing mapping data along the desired navigational path, projecting 3D obstacles onto a virtual plane, representing complex obstacle data with filtered geometric clusters and performing geometrical extrusion analysis to generate a collision-free solution. Collaboration with the Pilot-In-Command (PIC) is opportunistic and bidirectional allowing the PIC to control the aggressiveness of the obstacle avoidance solution on-the-fly easily adapting to the complexity of unknown indoor environments.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124432351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}