Cross-season vehicle localization using bag of local 3D features

Takahashi, Yoshiki; Kanji, Tanaka; Yichu, Fang

doi:10.1109/itsc.2017.8317606

Cited by 4 publications

(1 citation statement)

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“…With the recent advances in sparse coding theory, there is a push toward the representation of the higher-level features [60,61,64]. A more compact feature representation can be obtained by using the encoding technique of the mid-level feature, such as the Bag-Of-Visual-Words (BOW), Latent Dirichlet Allocation (LDA), Fisher vector (FV), etc.…”

Section: A Traditional Machine Learning Methodsmentioning

confidence: 99%

A Dual Attention Neural Network for Airborne LiDAR Point Cloud Semantic Segmentation

Xiao

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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With the development of airborne light detection and ranging (LiDAR) technology, it has become a common and efficient way to collect large-scale 3D spatial information. However, efficient and automatic semantic segmentation of LiDAR data, in the form of 3D point clouds, remains a persistent challenge. To address this, a dual attention neural network (DA-Net) is proposed, consisting of two different blocks, namely augmented edge representation (AER) and elevation attentive pooling (EAP). First, the AER can adaptively represent local orientation and position, thereby effectively enhancing geometric information. Second, the captured local features of centroid points are utilized to further encode discriminative features using the EAP with the learned attention scores. Finally, a location homogeneity (LH) module is devised to explore the long-range relationship in an encoder-decoder network. Benefiting from the dual attention module, geometric information hidden in unorganized point clouds can be effectively propagated. Besides, the LH forces the network to pay attention to the semantic consistency of elevated objects, which facilitates both point-and object-level point cloud semantic segmentation for scene understanding. A benchmark dataset is used to assess the proposed method, which achieves an overall accuracy of 85.98% and an average F1 score of 72.31%. In addition, comparisons with other latest deep learning methods on the 2019 Data Fusion Contest dataset further demonstrate the robustness and generalization ability of the proposed method.

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Section: A Traditional Machine Learning Methodsmentioning

confidence: 99%