• Acta Optica Sinica
  • Vol. 45, Issue 6, 0628002 (2025)
Chenguang Dai, Yingjian Zhang, Hongliang Ji*, Ruqin Zhou..., Zhenchao Zhang, Jinhao Lu and Siyi Wang|Show fewer author(s)
Author Affiliations
  • School of Geospatial Information Engineering, Information Engineering University, Zhengzhou 450001, Henan , China
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    DOI: 10.3788/AOS240972 Cite this Article Set citation alerts
    Chenguang Dai, Yingjian Zhang, Hongliang Ji, Ruqin Zhou, Zhenchao Zhang, Jinhao Lu, Siyi Wang. Place Recognition Method Based on Feature Fusion for LiDAR Point Clouds[J]. Acta Optica Sinica, 2025, 45(6): 0628002 Copy Citation Text show less
    References

    [1] Chang Y H, Chen N S, Rao L et al. Lidar point cloud descriptor with rotation and translation invariance in dynamic environment[J]. Acta Optica Sinica, 42, 2401007(2022).

    [2] Bosse M, Zlot R. Place recognition using keypoint voting in large 3D lidar datasets[C], 2677-2684(2013).

    [3] Zhuang Y, Jiang N, Hu H S et al. 3-D-laser-based scene measurement and place recognition for mobile robots in dynamic indoor environments[J]. IEEE Transactions on Instrumentation and Measurement, 62, 438-450(2013).

    [4] Steder B, Grisetti G, Burgard W. Robust place recognition for 3D range data based on point features[C], 1400-1405(2010).

    [5] Guo J D, Borges P V K, Park C et al. Local descriptor for robust place recognition using LiDAR intensity[J]. IEEE Robotics and Automation Letters, 4, 1470-1477(2019).

    [6] Luo L, Cao S Y, Han B et al. BVMatch: lidar-based place recognition using bird’s-eye view images[J]. IEEE Robotics and Automation Letters, 6, 6076-6083(2021).

    [7] He L, Wang X L, Zhang H. M2DP: a novel 3D point cloud descriptor and its application in loop closure detection[C]. Republic of Korea, 231-237(2016).

    [8] Kim G, Kim A. Scan context: egocentric spatial descriptor for place recognition within 3D point cloud map[C], 4802-4809(2018).

    [9] Li L, Kong X, Zhao X R et al. SSC: semantic scan context for large-scale place recognition[C], 2092-2099(2021).

    [10] Zhang Y J, Shi P C, Li J Y. LiDAR-based place recognition for autonomous driving: a survey[EB/OL]. http:∥arxiv.org/abs/2306.10561v2

    [11] Uy M A, Lee G H. PointNetVLAD: deep point cloud based retrieval for large-scale place recognition[C], 4470-4479(2018).

    [12] Zhang W X, Xiao C X. PCAN: 3D attention map learning using contextual information for point cloud based retrieval[C], 12428-12437(2019).

    [13] Liu Z, Zhou S B, Suo C Z et al. LPD-Net: 3D point cloud learning for large-scale place recognition and environment analysis[C], 2831-2840(2019).

    [14] Sun Q, Liu H Y, He J et al. DAGC: employing dual attention and graph convolution for point cloud based place recognition[C], 8-11(2020).

    [15] Xia Y, Xu Y S, Li S et al. SOE-Net: a self-attention and orientation encoding network for point cloud based place recognition[C], 11343-11352(2021).

    [16] Komorowski J. MinkLoc3[C], 1789-1798(2021).

    [17] Vidanapathirana K, Ramezani M, Moghadam P et al. LoGG3D-Net: locally guided global descriptor learning for 3D place recognition[C], 2215-2221(2022).

    [18] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https:∥arxiv.org/abs/1706.03762

    [19] Hui L, Yang H, Cheng M M et al. Pyramid point cloud transformer for large-scale place recognition[C], 6078-6087(2021).

    [20] Zhou Z C, Zhao C, Adolfsson D et al. NDT-transformer: large-scale 3D point cloud localisation using the normal distribution transform representation[C], 5654-5660(2021).

    [21] Ma J Y, Zhang J, Xu J T et al. OverlapTransformer: an efficient and rotation-invariant transformer network for LiDAR-based place recognition[EB/OL]. https:∥arxiv.org/pdf/2203.03397

    [22] Tian G X, Zhao J Q. Rotation-invariant LiDAR-based localization method based on feature pyramid and vector neural network[C], 1198-1201(2024).

    [23] Kong X, Yang X M, Zhai G Y et al. Semantic graph based place recognition for 3D point clouds[C], 8216-8223(2020).

    [24] Li L, Kong X, Zhao X R et al. RINet: efficient 3D lidar-based place recognition using rotation invariant neural network[J]. IEEE Robotics and Automation Letters, 7, 4321-4328(2022).

    [25] Chen X, Läbe T, Milioto A et al. OverlapNet: loop closing for LiDAR-based SLAM[C], 12-16(2020).

    [26] Zhang Y J, Dai C G, Zhou R Q et al. Scene overlap prediction for LiDAR-based place recognition[J]. IEEE Geoscience and Remote Sensing Letters, 20, 6502305(2023).

    [27] Zhang Z Y, Hua B S, Yeung S K. RIConv++: effective rotation invariant convolutions for 3D point clouds deep learning[J]. International Journal of Computer Vision, 130, 1228-1243(2022).

    [28] Arandjelović R, Gronat P, Torii A et al. NetVLAD: CNN architecture for weakly supervised place recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1437-1451(2018).

    [29] Ali-Bey A, Chaib-Draa B, Giguére P. MixVPR: feature mixing for visual place recognition[C], 2997-3006(2023).

    [30] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C], 3354-3361(2012).

    Chenguang Dai, Yingjian Zhang, Hongliang Ji, Ruqin Zhou, Zhenchao Zhang, Jinhao Lu, Siyi Wang. Place Recognition Method Based on Feature Fusion for LiDAR Point Clouds[J]. Acta Optica Sinica, 2025, 45(6): 0628002
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