Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping

Tanzmeister, Georg; Steyer, Sascha

doi:10.1109/mfi.2016.7849494

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…In this work, different lidar and radar sensors are used, which are described in more detail in Section VII. To take the different latencies and asynchronous measurement time instances of the individual sensors into account, all measurement data are buffered and then fused using an interval alignment based on a trigger sensor as proposed in [46].…”

Section: B Measurement Grid Fusionmentioning

confidence: 99%

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Steyer

Lenk

Kellner

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Object tracking is crucial for planning safe maneuvers of mobile robots in dynamic environments, in particular for autonomous driving with surrounding traffic participants. Multistage processing of sensor measurement data is thereby required to obtain abstracted high-level objects, such as vehicles. This also includes sensor fusion, data association, and temporal filtering. Often, an early-stage object abstraction is performed, which, however, is critical, as it results in information loss regarding the subsequent processing steps. We present a new grid-based object tracking approach that, in contrast, is based on already fused measurement data. The input is thereby pre-processed, without abstracting objects, by the spatial grid cell discretization of a dynamic occupancy grid, which enables a generic multi-sensor detection of moving objects. On the basis of already associated occupied cells, presented in our previous work, this paper investigates the subsequent object state estimation. The object pose and shape estimation thereby benefit from the freespace information contained in the input grid, which is evaluated to determine the current visibility of extracted object parts. An integrated object classification concept further enhances the assumed object size. For a precise dynamic motion state estimation, radar Doppler velocity measurements are integrated into the input data and processed directly on the object-level. Our approach is evaluated with real sensor data in the context of autonomous driving in challenging urban scenarios.

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Section: B Measurement Grid Fusionmentioning

confidence: 99%

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Steyer

Lenk

Kellner

et al. 2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

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“…Sensor fusion is a technique for improving the results of data from an individual sensor through combining it with data from another sensor. This technique is used for many types of sensors and data (Duraisamy, Bertolucci, Loehlein, & Schwarz, ; Romero, Borges, Elfes, & Pfrunder, ; Tanzmeister & Steyer, ; Zhang, Zhong, Wu, & Qian, ). Although GPR data for condition mapping has not been fused with camera data for crack detection, research on fusing GPR data with other sensors does exist (Boubaki, Saintenoy, & Tucholka, ; Larionova, Marques, & Almeida, ; Nath & Bhuiyan, ), and research on fusion of other NDE sensors also exists (Heideklang & Shokouhi, ; Khan, Ramuhalli, & Dass, ; Tian, Sophian, Taylor, & Rudlin, ; Lu & Michaels, ).…”

Section: Nde Sensor Fusionmentioning

confidence: 99%

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

Gibb

et al. 2018

Journal of Field Robotics

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Civil infrastructure inspection is crucial to maintaining the quality of that infrastructure, which has a great impact on the economy. Performing this inspection is costly work that requires workers to be trained on how to use varying technologies, which can be error prone when performed manually and can result in damage to the infrastructure in some cases. For this reason, nondestructive evaluation (NDE) sensors are preferred for civil infrastructure inspection as they can perform the necessary inspection without damaging the infrastructure. In this paper, we develop a fully autonomous robotic system capable of real-time data collection and quasi-real-time data processing. The robotic system is equipped with several NDE sensors that allow for a sensor fusion method to be developed that successfully minimizes inspection time while performing adequate inspection of areas that require more in-depth data to be collected. A detailed discussion of the inspection framework developed for this robotic system, and the dual navigation modes for both indoor and outdoor autonomous navigation is presented. The developed robotic system is deployed to inspect several infrastructures (e.g., parking garages, bridges) at and near by the University of Nevada, Reno campus. K E Y W O R D Sconcrete inspection, field robots, non-destructive inspection SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Gibb S, La HM, Le T, Nguyen L, Schmid R, Pham H. Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection.

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“…It ensures that the inputs of data fusion are the measurements of the same target at the same time. Some temporal alignment methods have been proposed up to now, e.g., least square method [3], optimal linearization approach [4], grid based representation [5]. In order to overcome the asynchronous problem, a priori knowledge of the target motion model named nearly-constant-velocity motion model was researched in [6].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Alignment and Trajectory Matching for Netted Radar Without Prior Spatial Information and Time Delay

et al. 2020

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For netted radars, a new alignment algorithm without prior spatial information (i.e., locations and attitudes of radars) and time delay is proposed to keep the spatio-temporal alignment. The unknown parameters to be estimated include the rotation matrix, the translation vector and the delay between radar stations in this paper. The minimum error function in unified space and time coordinate system is established based on the target trajectory measured by each radar. Firstly, the alternating spatio-temporal alignment method is used to estimate the spatial and temporal parameters, and its statistical performance is compared to the Cramer-Rao bound. Even under severe conditions in which each radar in the network can only observe part of the trajectory, the proposed algorithm can still be adopted to estimate the alignment parameters and complete the trajectory. Then, a trajectory matching algorithm based on random sample consensus (RANSAC) is proposed for multitarget. The corresponding relationship between trajectories is established through minimizing sum of paired trajectory error, and multitarget trajectories from each radar are matched. Finally, the spatio-temporal parameters are refined by all matched trajectory pairs. Simulation results show that the tracking information from different radars is transformed into a unified coordinate after spatiotemporal alignment. Moreover, the registration error is reduced and the tracking accuracy is improved.

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Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping

Cited by 11 publications

References 15 publications

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

Spatio-Temporal Alignment and Trajectory Matching for Netted Radar Without Prior Spatial Information and Time Delay

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