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Point-Cloud-Based Place Recognition Using CNN Feature Extraction
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DOI：10.1109/JSEN.2019.2937740.png)
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Abstract
En 中文
This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction. By using a convolutional neural network pre-trained on color images to extract features from a range image without fine-tuning on extra range images, significant improvement has been observed when compared to using hand-crafted features. The resulting system is illumination invariant, rotation invariant and robust against moving objects that are unrelated to the place identity. Apart from the system itself, we also bring to the community a new place recognition dataset (dataset is available at https://drive.google.com/file/d/1TYga-55gvyMKAaUqX_lxJQr2A5ZAOC00/view?usp=sharing) containing both point cloud and grayscale images covering a full 360 degrees environmental view. In addition, the dataset is organized in such a way that it facilitates experimental validation with respect to rotation invariance or robustness against unrelated moving objects separately.
Keywords:
Place recognition
point cloud
CNN
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