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3D Lidar Data Segmentation Using a Sequential Hybrid Method

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Informatics in Control, Automation and Robotics (ICINCO 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 495))

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Abstract

This chapter proposes a sequential hybrid method for 3D Lidar data segmentation. The presented approach provides more reliable results against the under-segmentation issue, i.e., assigning several objects to one segment, by combining spatial and temporal information to discriminate nearby objects in the data. For instance, it is common for pedestrians to get under-segmented with their neighboring objects. Combining temporal and spatial cues allow us to resolve such ambiguities. After getting the temporal features, we propose a sequential hybrid approach using the mean-shift method and a sequential variant of distance dependent Chinese Restaurant Process (ddCRP). The segmentation blobs are spatially extracted from the scene with a connected components algorithm. Then, as a post-processing, the mean-shift seeks the number of possible objects in the state space of each blob. If the mean-shift algorithm determines an under-segmentation, the sequential ddCRP performs the final partition in this blob. Otherwise, the queried blob remains the same and it is assigned as a segment. Compared to the other recent methods in the literature, our framework significantly reduces the under-segmentation errors while running in real time.

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Acknowledgements

We acknowledge the support by the EU’s Seventh Framework Programme under grant agreement no. 607400 (TRAX, Training network on tRAcking in compleX sensor systems) http://www.trax.utwente.nl/.

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Authors and Affiliations

  1. Cognitive Mobile Systems, Fraunhofer FKIE, Fraunhoferstr. 20, 53343, Wachtberg, Germany

    Mehmet Ali Çağrı Tuncer & Dirk Schulz

Authors
  1. Mehmet Ali Çağrı Tuncer
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  2. Dirk Schulz
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Corresponding author

Correspondence to Mehmet Ali Çağrı Tuncer .

Editor information

Editors and Affiliations

  1. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

  2. University of Paris-EST Créteil (UPEC), Vitry sur Seine, France

    Kurosh Madani

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Tuncer, M.A.Ç., Schulz, D. (2020). 3D Lidar Data Segmentation Using a Sequential Hybrid Method. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics . ICINCO 2017. Lecture Notes in Electrical Engineering, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-030-11292-9_26

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