Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Song, Wei; Zou, Shuanghui; Tian, Yifei; Fong, Simon; Cho, Kyungeun

doi:10.1186/s13673-018-0152-7

Cited by 24 publications

(12 citation statements)

References 30 publications

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“…To make full use of this reliable and long-lasting feature, pole extraction has aroused great interest in academia, especially in the research field of autonomous vehicle navigation and high-definition map (HD map)-making [ 40 , 41 , 42 ]. In Song et al [ 43 ], a point cloud is clustered, and then features such as eigen values, principle components of each cluster, are computed. Then, the feature vector is taken as the input of the back-propagation neural network to get the label of the belonging cluster.…”

Section: Methodsmentioning

confidence: 99%

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Liu

Chang

Niu

et al. 2020

Sensors

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Vision-based sensors such as LiDAR (Light Detection and Ranging) are adopted in the SLAM (Simultaneous Localization and Mapping) system. In the 16-beam LiDAR aided SLAM system, due to the difficulty of object detection by sparse laser data, neither the grid-based nor feature point-based solution can avoid the interference of moving objects. In an urban environment, the pole-like objects are common, invariant and have distinguishing characteristics. Therefore, it is suitable to bring more robust and reliable positioning results as auxiliary information in the process of vehicle positioning and navigation. In this work, we proposed a scheme of a SLAM system using a GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and LiDAR sensor using the position of pole-like objects as the features for SLAM. The scheme combines a traditional preprocessing method and a small scale artificial neural network to extract the pole-like objects in environment. Firstly, the threshold-based method is used to extract the pole-like object candidates from the point cloud, and then, the neural network is applied for training and inference to obtain pole-like objects. The result shows that the accuracy and recall rate are sufficient to provide stable observation for the following SLAM process. After extracting the poles from the LiDAR point cloud, their coordinates are added to the feature map, and the nonlinear optimization of the front end is carried out by utilizing the distance constraints corresponding to the pole coordinates; then, the heading angle and horizontal plane translation are estimated. The ground feature points are used to enhance the elevation, pitch and roll angle accuracy. The performance of the proposed navigation system is evaluated through field experiments by checking the position drift and attitude errors during multiple two-min mimic GNSS outages without additional IMU motion constrain such as NHC (Nonholonomic Constrain). The experimental results show that the performance of the proposed scheme is superior to that of the conventional feature point grid-based SLAM with the same back end, especially in congested crossroads where slow-moving vehicles are surrounded and pole-like objects are rich in the environment. The mean plane position error during two-min GNSS outages was reduced by 38.5%, and the root mean square error was reduced by 35.3%. Therefore, the proposed pole-like feature-based GNSS/IMU/LiDAR SLAM system can fuse condensed information from those sensors effectively to mitigate positioning and orientation errors, even in a short-time GNSS denied environment.

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Section: Methodsmentioning

confidence: 99%

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Liu

Chang

Niu

et al. 2020

Sensors

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“…The training loss function measures the difference between the predicted value and the true value [36]. Designing a reasonable and effective loss function is very important for the training of the target model.…”

Section: The Training Loss Function Of the Object Detection Networkmentioning

confidence: 99%

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

Cao

Chen

Gao

2020

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe main purpose of object detection is to identify and locate one or more effective targets from still image or video data. It comprehensively includes a variety of important techniques, such as image processing, pattern recognition, artificial intelligence and machine learning. It has broad application prospects in such areas such as road traffic accident prevention [1], warnings of dangerous goods in factories, military restricted area monitoring and advanced human-computer interaction [2,3]. Since the application scenarios of multi-target detection in the real world are usually complex and variable, balancing the relationship between accuracy and computing costs is a difficult task.The object detection process is traditionally established by manually extracting feature models, where the common features are represented by HOG (histogram of oriented AbstractObject detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convolutional object detection network to deal with the challenges faced by current methods. Our analysis demonstrates a strong performance on par, or even better, than state of the art methods. We use deep convolutional networks to obtain multi-scaled features, and add deformable convolutional structures to overcome geometric transformations. We then fuse the multi-scaled features by up sampling, in order to implement the final object recognition and region regress. Experiments prove that our suggested framework improves the accuracy of detecting small target objects with geometric deformation, showing significant improvements in the trade-off between accuracy and speed.

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“…Neural networks have a very wide range of applications, from video activity detection [22] and face recognition [23] to classification of 3D objects [24] and emotion classification [25].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Modelling email traffic workloads with RNN and LSTM models

Boukoros

Nugaliyadde

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities' servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

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Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Cited by 24 publications

References 30 publications

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

Pole-Like Object Extraction and Pole-Aided GNSS/IMU/LiDAR-SLAM System in Urban Area

An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks

Modelling email traffic workloads with RNN and LSTM models

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