Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design

Hanbiao Wang, G. Pottie, Kung Yao, and D. Estrin, “Entropy-based sensor selection heuristic for target localization,” in Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004 , 2004, pp. 36–45

2004

Earlier work this paper cites.

A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies,” Journal of Machine Learning Research , vol. 9, p. 235–284, 2008

2008

Earlier work this paper cites.

M. Naeem, S. Xue, and D. C. Lee, “Cross-entropy optimization for sensor selection problems,” in 2009 9th International Symposium on Communications and Information Technology , 2009, pp. 396–401

2009

Earlier work this paper cites.

A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition , 2012, pp. 3354–3361

Original

2012

Earlier work this paper cites.

C. Yang, L. Kaplan, E. Blasch, and M. Bakich, “Optimal placement of heterogeneous sensors for targets with gaussian priors,” IEEE Transactions on Aerospace and Electronic Systems , vol. 49, no. 3, pp. 1637–1653, 2013

2013

Earlier work this paper cites.

J. Ranieri, A. Chebira, and M. Vetterli, “Near-optimal sensor placement for linear inverse problems,” IEEE Transactions on Signal Processing , vol. 62, no. 5, pp. 1135–1146, 2014

2014

Earlier work this paper cites.

P. Rahimian and J. K. Kearney, “Optimal camera placement for motion capture systems,” IEEE transactions on visualization and computer graphics , vol. 23, no. 3, pp. 1209–1221, 2016

2016

Cited alongside, same era.

C. Jiang, Y. C. Soh, and H. Li, “Sensor placement by maximal projection on minimum eigenspace for linear inverse problems,” IEEE Transactions on Signal Processing , vol. 64, no. 21, pp. 5595–5610, 2016

2016

Cited alongside, same era.

J. Dybedal and G. Hovland, “Optimal placement of 3d sensors considering range and field of view,” in 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) , 2017, pp. 1588–1593

2017

Cited alongside, same era.

S. Mou, Y. Chang, W. Wang, and D. Zhao, “An optimal lidar configuration approach for self-driving cars,” arXiv preprint arXiv:1805.07843 , 2018

Original

2018

Cited alongside, same era.

C. Jiang, Z. Chen, R. Su, and Y. C. Soh, “Group greedy method for sensor placement,” IEEE Transactions on Signal Processing , vol. 67, no. 9, pp. 2249–2262, 2019

2019

Later among the works it cites.

A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 12 689–12 697

2019

Later among the works it cites.

A. Carballo, J. Lambert, A. Monrroy, D. Wong, P. Narksri, Y. Kitsukawa, E. Takeuchi, S. Kato, and K. Takeda, “Libre: The multiple 3d lidar dataset,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 1094–1101

2020

Later among the works it cites.

J. Dybedal and G. Hovland, “Gpu-based optimisation of 3d sensor placement considering redundancy, range and field of view,” in 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) , 2020, pp. 1484–1489

2020

Later among the works it cites.