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Point-cloud-based place recognition using CNN feature extraction [article]

Ting Sun, Ming Liu, Haoyang Ye, Dit-Yan Yeung
2018 arXiv   pre-print
This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction.  ...  Apart from the system itself, we also bring to the community a new place recognition dataset containing both point cloud and grayscale images covering a full 360^∘ environmental view.  ...  Our focus in this work is point-cloud-based feature extraction using a CNN.  ... 
arXiv:1810.09631v1 fatcat:sguqdmc5yrccnmav3xkegxvmje

3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey

Sumaira Manzoor, Sung-Hyeon Joo, Eun-Jin Kim, Sang-Hyeon Bae, Gun-Gyo In, Jeong-Won Pyo, Tae-Yong Kuc
2021 Sensors  
Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models  ...  This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves  ...  features LiDAR Point Cloud based extraction with CNN Large scale place recognition Lpd-net [83] Outdoor scenario with feature extraction using global LiDAR Point Cloud based descriptors Oriented recognition  ... 
doi:10.3390/s21217120 pmid:34770429 pmcid:PMC8587961 fatcat:qqoqlkaycbc4xk24j7j2nzqabe

CNN-BASED PLACE RECOGNITION TECHNIQUE FOR LIDAR SLAM

Y. Yang, S. Song, C. Toth
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To mitigate this problem, state-of-art Convolutional Neural Networks (CNNs)-based 3D descriptors may be directly applied to 3D point clouds.  ...  Place recognition in 3D point clouds is a challenging task which is traditionally handled with the aid of other sensors, such as camera and GPS.  ...  CNN-based classifier, NN and RF by using the same set of global feature descriptors extracted from testing datasets.  ... 
doi:10.5194/isprs-archives-xliv-m-2-2020-117-2020 fatcat:6wzdco6wcvcrdaooyvctmvh2bq

Learning 3D Segment Descriptors for Place Recognition [article]

Andrei Cramariuc, Renaud Dubé, Hannes Sommer, Roland Siegwart, Igor Gilitschenski
2018 arXiv   pre-print
In this work we leverage a segment extraction and matching approach to achieve place recognition in Light Detection and Ranging (LiDAR) based 3D point cloud maps.  ...  Most place recognition methods rely on images, point clouds, or a combination of both.  ...  Particularly, this work focuses on the SegMatch method for performing place recognition in 3D point clouds, which is based on comparing segments extracted from a point cloud with previously observed segments  ... 
arXiv:1804.09270v1 fatcat:vulys5bk7vc45oqc6solus6cvy

CNN-Based Classification for Point Cloud Object With Bearing Angle Image

Chien-Chou Lin, Chih-Hung Kuo, Hsin-Te Chiang
2021 IEEE Sensors Journal  
The main contribution of this paper is proposing an efficient recognition method that uses the information from point clouds only.  ...  Those individual point cloud objects are converted to bearing angle (BA) images. Then, a well-trained CNN is used to classify objects with BA images.  ...  While the local feature-based methods use the 3D features derived by a point and its neighbors, the global feature-based approaches use the features extracted from the whole objects for recognition, e.g  ... 
doi:10.1109/jsen.2021.3130268 fatcat:4n2wpu3ykbdexmv6jcaph33lim

LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis [article]

Zhe Liu and Shunbo Zhou and Chuanzhe Suo and Yingtian Liu and Peng Yin and Hesheng Wang and Yun-Hui Liu
2019 arXiv   pre-print
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the  ...  We implement the proposed global descriptor in solving point cloud based retrieval tasks to achieve the large-scale place recognition.  ...  Current solutions for environment analysis and place recognition mainly fall into two categories, image-based and 3D point cloud-based.  ... 
arXiv:1812.07050v2 fatcat:wryxaqevtjh4po4hk2rvb4wn5a

Activity Recognition Based on Millimeter-Wave Radar by Fusing Point Cloud and Range–Doppler Information

Yuchen Huang, Wei Li, Zhiyang Dou, Wantong Zou, Anye Zhang, Zan Li
2022 Signals  
We adopt a CNN–LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range–Doppler.  ...  In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range–Doppler information from radar echo to extract activity features.  ...  For example, in [10] , the experimenters converted the obtained point cloud data into micro-Doppler features and used CNN to extract the features to classify actions.  ... 
doi:10.3390/signals3020017 fatcat:nrelwuklyraknmyhsiqnyuhlza

PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition [article]

Yuheng Lu, Fan Yang, Fangping Chen, Don Xie
2020 arXiv   pre-print
Place recognition is one of the hot research fields in automation technology and is still an open issue, Camera and Lidar are two mainstream sensors used in this task, Camera-based methods are easily affected  ...  to fuse the features of image and point cloud, and mine the complementary information between the two.  ...  RELATED WORK In this section, we review other place recognition works, including image-based, point cloud-based and image and point cloud fusion-based place recognition methods.  ... 
arXiv:2008.00658v1 fatcat:ognqwld5zredrir3x2nsesdc6m

Viewpoint invariant semantic object and scene categorization with RGB-D sensors

Hasan F. M. Zaki, Faisal Shafait, Ajmal Mian
2018 Autonomous Robots  
We address these problems and propose a generic deep learning framework based on a pre-trained convolutional neural network (CNN), as a feature extractor for both the colour and depth channels.  ...  Extensive evaluations on four RGB-D object and scene recognition datasets demonstrate that our HP-CNN and HP-CNN-T consistently outperforms state-of-the-art methods for several recognition tasks by a significant  ...  input encoding a) depth image and b) point cloud object-centric encoding before feature extraction.  ... 
doi:10.1007/s10514-018-9776-8 fatcat:spg6rdzgbrhpxpbuedqr2th2pe

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

Muhammad Rabani Mohd Romlay, Azhar Mohd Ibrahim, Siti Fauziah Toha, Philippe De Wilde, Ibrahim Venkat, Jie Zhang
2021 PLoS ONE  
To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction  ...  The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection.  ...  Thus, this paper proposes an object recognition system with multiple feature extraction based on segregated clusters from LiDAR point clouds.  ... 
doi:10.1371/journal.pone.0256665 pmid:34432855 pmcid:PMC8386852 fatcat:b6fwtwjiknce3d5ivgtcickrqq

Place recognition survey: An update on deep learning approaches [article]

Tiago Barros, Ricardo Pereira, Luís Garrote, Cristiano Premebida, Urbano J. Nunes
2022 arXiv   pre-print
This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning.  ...  The contributions of this work are twofold: surveying recent sensors such as 3D LiDARs and RADARs, applied in place recognition; and categorizing the various DL-based place recognition works into supervised  ...  [13] uses 3D point clouds to address the global localization problem, proposing a place recognition and metric pose estimation approach.  ... 
arXiv:2106.10458v3 fatcat:bbfv4qympffaphojhxkc4og4am

CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS

Y. Mori, K. Kohira, H. Masuda
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The first method used only point-clouds, the second used only images, and the third used both point-clouds and images.  ...  The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network.  ...  In our method, poles and target objects were extracted from point-clouds, and feature values were calculated using point-clouds.  ... 
doi:10.5194/isprs-archives-xlii-2-731-2018 fatcat:grs6ojnanbghjlrjgmzcyivcza

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

Lukas Schaupp, Mathias Burki, Renaud Dube, Roland Siegwart, Cesar Cadena
2019 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans.  ...  These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods.  ...  [11] transformed a point cloud to a range or bearing-angle based image by extracting local-invariant ORB [12] features for database matching.  ... 
doi:10.1109/iros40897.2019.8968094 dblp:conf/iros/SchauppBDSC19 fatcat:chckxkzkgfekvmefdb66emrai4

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios [article]

Lukas Schaupp, Mathias Bürki, Renaud Dubé, Roland Siegwart, Cesar Cadena
2019 arXiv   pre-print
We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans.  ...  These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods.  ...  [12] transformed a point cloud to a range or bearing-angle based image by extracting local-invariant ORB [13] features for database matching.  ... 
arXiv:1903.07918v1 fatcat:2scnozweabepziyh6cfl6vxph4

Pointwise CNN for 3D Object Classification on Point Cloud

Wei Song, Zishu Liu, Yifei Tian, Simon Fong
2021 Journal of Information Processing Systems  
Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions.  ...  Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset.  ...  [24] converted point clouds to a surfacecondition-feature map for feature extraction using an autoencoder. The geometric features extracted work well even with extremely noisy data. Su et al.  ... 
doi:10.3745/jips.02.0160 dblp:journals/jips/SongLTF21 fatcat:jeq2oyvb4rhupgcqzuntygrg4u
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