Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area

Kim, Hyungjin; Liu, Bingbing; Goh, Chi Yuan; Lee, Serin; Myung, Hyun

doi:10.1109/lra.2017.2673868

Cited by 80 publications

(46 citation statements)

References 14 publications

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“…6 Despite these drawbacks, there is no alternative to HD maps for navigation in the current state of technology. 14 In regards to localizing the vehicle without high precision GPS signals, techniques have been proposed in recent years but still rely heavily on prior detailed map information, [20][21][22][23][24] or remain limited in terms of real-time mapping of the full roadway scene. [26][27][28][29] Finally, SLAM techniques, such as FAB-MAP and SeqSLAM, can be used to alleviate the need for HD maps and GPS signals.…”

Section: Resultsmentioning

confidence: 99%

“…The problems of navigating without GPS and a highly detailed map can be addressed separately in a less computationally expensive manner. Techniques have been proposed in recent years based on accurate self‐localization in mapped environments in an attempt to reduce the vehicle's reliance upon GPS . However, these methods still rely heavily on a priori map information, as they require either a lidar or vision sensor layer to be included in the environmental map.…”

Section: Introductionmentioning

confidence: 99%

“…Techniques have been proposed in recent years based on accurate self-localization in mapped environments in an attempt to reduce the vehicle's reliance upon GPS. [20][21][22][23][24] However, these methods still rely heavily on a priori map information, as they require either a lidar or vision sensor layer to be included in the environmental map. Likewise, sufficient localization in an extended GPS blackout can be provided by a posterior pose algorithm, which augments GPS and an inertial navigation system with vision-based measurements of nearby lanes and stop lines referenced to a known map of environmental features.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high‐definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map‐based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real‐time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass‐based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

View full text Add to dashboard Cite

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“…Since both sensor results are separately used in the Kalman filter, no registration between the implemented sensors is needed. Kim et al [39] proposed a fusion algorithm based on a particle filter using vertical and road intensity information for robust vehicle localization in a large-scale urban area. However, filtering methods lack in terms of dynamic behavior and the algorithm performance varies with the change of state matrixes [38,39].…”

Section: Sensor Fusion and Filteringmentioning

confidence: 99%

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

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Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

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“…Such applications include 3D map reconstruction for intelligent vehicle navigation and control [1][2][3][4], 3D city modeling [5][6][7], city visualizations [8], road asset inventories [9], railway modeling [10], vegetation detection and urban forest inventories [11][12][13]. As the primary provider of static environment information for the intelligent vehicles, maps constructed through MMSs have even been considered as "virtual sensors" [14] that would enable long-distance autonomous navigation [15].…”

Section: Introductionmentioning

confidence: 99%

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Javanmardi

et al. 2017

Remote Sensing

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Abstract:Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle's self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

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Robust Vehicle Localization Using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area

Cited by 80 publications

References 14 publications

Estimation and navigation methods with limited information for autonomous urban driving

Estimation and navigation methods with limited information for autonomous urban driving

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

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