Joint tracking and non-parametric shape estimation of arbitrary extended objects

Wyffels, Kevin; Campbell, Mark

doi:10.1109/icra.2015.7139663

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…Non-parametric object tracking: Several methods align tracked object point clouds with new segmented scans, then append the segment to and enrich the 3D model [28], [29], [10]. Wyffels and Campbell [28] lay a probabilistic method to estimate motion and shape for a single extended object using simulated LiDAR data, but do not experiment with realworld datasets and do not add camera. Held et al, [10] track 2D velocity vectors in real-time and accumulate points to form a dense model for each track.…”

Section: Related Workmentioning

confidence: 99%

Velocity and shape from tightly-coupled LiDAR and camera

Daraei

Manduchi

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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In this paper, we propose a multi-object tracking and reconstruction approach through measurement-level fusion of LiDAR and camera. The proposed method, regardless of object class, estimates 3D motion and structure for all rigid obstacles. Using an intermediate surface representation, measurements from both sensors are processed within a joint framework. We combine optical flow, surface reconstruction, and point-to-surface terms in a tightly-coupled non-linear energy function, which is minimized using Iterative Reweighted Least Squares (IRLS). We demonstrate the performance of our model on different datasets (KITTI with Velodyne HDL-64E and our collected data with 4-layer ScaLa Ibeo), and show an improvement in velocity error and crispness over state-of-theart trackers.

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Section: Related Workmentioning

confidence: 99%

Velocity and shape from tightly-coupled LiDAR and camera

Daraei

Manduchi

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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“…As the number of particles increases, the estimates of the intractable integrals converge to the true values; hence, the PF estimate converges to the optimal solution in case the associated models are correct. Recent works exploiting different kinds of PFs for automated driving include [17,18,19]. One of the main drawbacks of PFs is the exponential scaling of the number of required particles with the dimension of the state space.…”

Section: Related Workmentioning

confidence: 99%

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Elfring

Appeldoorn

Dries

et al. 2016

Sensors

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The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture.

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Real-Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation

Lee

Seo

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Joint tracking and non-parametric shape estimation of arbitrary extended objects

Cited by 10 publications

References 17 publications

Velocity and shape from tightly-coupled LiDAR and camera

Velocity and shape from tightly-coupled LiDAR and camera

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Real-Time Object Tracking in Sparse Point Clouds Based on 3D Interpolation

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