Multimodal sensor-based semantic 3D mapping for a large-scale environment

Jeong, Jongmin; Yoon, Tae Sung; Park, Jin Bae

doi:10.1016/j.eswa.2018.03.051

Cited by 34 publications

(25 citation statements)

References 31 publications

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“…useful to help a segmentation algorithm in order to obtain a semantic segmentation of the scene (Rusu et al, 2009;Jeong et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Sensor-Topology Based Simplicial Complex Reconstruction From Mobile Laser Scanning

Guinard¹,

Vallet²

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Nous présentons une nouvelle méthode pour la reconstruction de complexes simpliciaux (ensembles de points, segments et triangles) à partir de nuages de points 3D obtenus par LiDAR mobile. Notre méthode utilise la topologie inhérente au capteur LiDAR pour définir une relation spatiale entre les points. Pour cela, nous examinons chaque connexion possible entre points, pondérée en fonction de sa distance au capteur, et les filtrons en privilégiant les structures collinéaires, ou perpendiculaires aux impulsions du laser. Ensuite, nous créons et filtrons des triangles pour chaque triplet de segments connectés entre eux, en fonction de leur coplanarité locale. Nous comparons nos résultats à une reconstruction non pondérée d'un complexe simplicial. AbstractWe propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points, weighted according to its distance to the sensor, and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create and filter triangles for each triplet of self-connected edges and according to their local planarity. We compare our results to an unweighted simplicial complex reconstruction.

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“…useful to help a segmentation algorithm in order to obtain a semantic segmentation of the scene (Rusu et al, 2009;Jeong et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Sensor-Topology Based Simplicial Complex Reconstruction From Mobile Laser Scanning

Guinard¹,

Vallet²

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ref. [ 34 ] introduced semantic 3-D mapping by the data association between 3-D map from the point cloud and CNN based-segmented labeled images. Ref.…”

Section: Related Workmentioning

confidence: 99%

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

Cho

Kim

Park

et al. 2020

Sensors

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LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

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“…In this way, after the fusion of the point clouds and RGB images, the mobile platform can perceive either the geometric information or the corresponding semantic information. The effective fusion benefits the 3D object detection [1][2][3][4] and semantic mapping tasks [5][6][7]. Thus, extrinsic calibration between LiDARs and cameras, as the precondition of data fusion, has been a crucial scientific problem.…”

Section: Introductionmentioning

confidence: 99%

CFNet: LiDAR-Camera Registration Using Calibration Flow Network

Wang

Dou

et al. 2021

Sensors

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As an essential procedure of data fusion, LiDAR-camera calibration is critical for autonomous vehicles and robot navigation. Most calibration methods require laborious manual work, complicated environmental settings, and specific calibration targets. The targetless methods are based on some complex optimization workflow, which is time-consuming and requires prior information. Convolutional neural networks (CNNs) can regress the six degrees of freedom (6-DOF) extrinsic parameters from raw LiDAR and image data. However, these CNN-based methods just learn the representations of the projected LiDAR and image and ignore the correspondences at different locations. The performances of these CNN-based methods are unsatisfactory and worse than those of non-CNN methods. In this paper, we propose a novel CNN-based LiDAR-camera extrinsic calibration algorithm named CFNet. We first decided that a correlation layer should be used to provide matching capabilities explicitly. Then, we innovatively defined calibration flow to illustrate the deviation of the initial projection from the ground truth. Instead of directly predicting the extrinsic parameters, we utilize CFNet to predict the calibration flow. The efficient Perspective-n-Point (EPnP) algorithm within the RANdom SAmple Consensus (RANSAC) scheme is applied to estimate the extrinsic parameters with 2D–3D correspondences constructed by the calibration flow. Due to its consideration of the geometric information, our proposed method performed better than the state-of-the-art CNN-based methods on the KITTI datasets. Furthermore, we also tested the flexibility of our approach on the KITTI360 datasets.

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Multimodal sensor-based semantic 3D mapping for a large-scale environment

Cited by 34 publications

References 31 publications

Sensor-Topology Based Simplicial Complex Reconstruction From Mobile Laser Scanning

Sensor-Topology Based Simplicial Complex Reconstruction From Mobile Laser Scanning

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving

CFNet: LiDAR-Camera Registration Using Calibration Flow Network

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