Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection

Matti, Damien; Ekenel, Hazım Kemal; Thiran, Jean‐Philippe

doi:10.48550/arxiv.1710.06160

Cited by 1 publication

(1 citation statement)

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“…In addition, the method includes a sequential classification procedure to reduce the occurrence of false positives. Furthermore, it can be seen generally that the most used sensors for object detection, classification and tracking based on machine learning techniques are 3D LiDARs [5,19,68], cameras 5 or a combination of LiDARs and cameras [35,43], while 2D laser rangefinders are usually avoided for this task despite their convenience, as they provide only partial contour information. This fact poses the challenge of how to make use of sparse data with limited information content, while still achiving a system with robust tracking capabilities and a small number of false positive detections.…”

Section: Related Workmentioning

confidence: 99%

Detection, localisation and tracking of pallets using machine learning techniques and 2D range data

Mohamed

Capitanelli

Mastrogiovanni

et al. 2019

Neural Comput & Applic

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The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labeled 2DThe research leading to these results has received funding from the POR/FESR Liguria regional funding scheme, under grant agreement n. 56 (AIRONE).

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