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LIDAR（Light Detection and Ranging）
LIDAR (Light Detection and Ranging, LIDAR) is a technology that uses laser light to measure the distance to an object and to accurately determine the surrounding environment and the three-dimensional shape of an object. . This technology is used in a variety of fields, including automated driving, topographical surveying, archaeology and construction.
A LIDAR system consists of the following basic elements.
1. laser emitters: pulses of laser light are emitted in short bursts, the wavelength of the emitted light being mainly in the near-infrared (approx. 900-1500 nm); 2. detectors (sensors): these are used to detect the laser beam and to detect the laser beam’s position in the sky.
2. Detector (sensor): the emitted laser beam is reflected off an object and the reflected light is received.
3. Time of Flight (TOF) measurement: the time difference between the emission and return of the reflected light is measured and the distance between the laser emission point and the object is calculated on the basis of this time difference.
4. data processing systems: 3D mapping of numerous measurement points within the laser beam’s range, and computer analysis and reconstruction of the object’s shape and surrounding environment.
The main applications of LIDAR include.
1. automated driving: LIDAR plays an important role as an environmental awareness system for automated vehicles: with LIDAR, vehicles can determine the distance and position of surrounding vehicles, pedestrians and obstacles in real time, enabling them to drive safely.
2. topography: LIDAR is frequently used for topographical surveying to collect highly accurate terrain data. LIDAR mounted on aircraft and drones can map forest, mountain and urban terrain in great detail.
3. construction and infrastructure management: LIDAR technology creates 3D models of construction sites and urban infrastructure and is used for monitoring and managing structures. LIDAR data can also be used in the design and planning stages of buildings for accurate measurement and simulation.
4. archaeology: in archaeology, LIDAR can be used to non-invasively find buried archaeological sites and ancient structures beneath the ground, and LIDAR can accurately capture buried remains, even in land covered with forest and vegetation.
5. weather observation: LIDAR is used to measure the density of particles and gases in the atmosphere and wind movement, and has applications in weather forecasting and environmental monitoring.
Advantages of LIDAR include: ‘high accuracy’, where LIDAR can measure distances to objects with millimetre accuracy and build detailed 3D models; ‘long range measurability’, where LIDAR can measure distances from a few hundred metres to several kilometres away, allowing for a wide range of terrain and structures; and ‘high accuracy’, where LIDAR can accurately measure in darkness, fog and rain. The system can also measure accurately in darkness, fog and rain, making it usable in a wide range of environmental conditions, and it is ‘all-weather’ and ‘high-speed data collection’, which means it can collect a large number of measurement points in a short time and generate 3D data in real time.
Disadvantages include the high cost of installing and operating the system, the fact that it is easily influenced by the material of the object and the reflective properties of its surface, making it difficult to measure hard-to-reflect materials such as glass and water, and the fact that the data obtained is extremely large and requires advanced analysis to process it into meaningful information. The data obtained is very large and requires sophisticated analysis in order to process it into meaningful information.
LIDAR and AI technology
The characteristics of the combination of LIDAR and AI technologies with these features are considered. In particular, the case of combining LIDAR with generative AI and GNNs is described here.
<Combination with generative AI>
The combination of LIDAR technology and generative AI enables more advanced data analysis and applications as follows.
1. data completion and reconstruction: by using generative AI, even when LIDAR data is missing, AI can infer from past data and similar environments, complement the missing parts and generate accurate 3D models.
2. enhanced automated driving: the AI analyses LIDAR data to improve the accuracy of the vehicle’s real-time understanding of its surroundings. Generative AI predicts object movements and human behaviour to help vehicles drive more safely and efficiently.
3. automating environmental monitoring: in environmental monitoring, generative AI can be used to automate the detection of anomalies from LIDAR data and the prediction of long-term changes. For example, topographical changes and deforestation can be monitored to help protect the environment.
<Combination with GNN>
The combination of LIDAR technology and Graph Neural Networks (GNN) enables advanced analysis and inference of LIDAR data, where LIDAR scans the surrounding environment in 3D and generates a huge amount of point cloud data (3D point cloud), By treating this point cloud data as a graph structure and applying GNNs, more efficient and advanced analysis can be expected, as shown below.
1. 3D object recognition and classification: point cloud data acquired by LIDAR is usually very dense and noisy, making classification and object recognition difficult using conventional methods GNN processes point cloud data as a graph structure and models each point as a node and the relationships between nodes as edges. By doing so, it understands and classifies the characteristics of 3D objects. In addition, automated vehicles need to identify surrounding vehicles, pedestrians and obstacles in real time, and LIDAR analysis using GNNs enables these objects to be classified with high accuracy and adapt to the dynamic environment.
2. segmentation of 3D point clouds: semantic segmentation of point cloud data is an important task in automated driving and robotics; GNNs can not only capture the local structure of point clouds, but also take into account long-range dependencies and contextual information, which allows buildings, vehicles, roads, natural objects etc., can be clearly segmented to improve understanding of the environment. This could, for example, be used in infrastructure management on construction sites and in urban areas, where GNNs can be used to accurately identify structures such as roads, buildings and pipes to help build a digital twin.
3. dynamic object tracking and prediction: combining LIDAR data with GNNs enables more precise tracking of the movements of surrounding dynamic objects (e.g. vehicles, pedestrians) and prediction of their future location and behaviour GNNs can learn movement patterns to effectively capture interactions between objects with complex dependencies, thereby improving prediction accuracy. learning and improve prediction accuracy. This technology is particularly important for the safe operation of automated vehicles, where GNNs can be used to create advanced predictive models that can adapt to difficult-to-predict pedestrian behaviour and complex traffic situations.
4. anomaly detection and automatic obstacle avoidance: data acquired by LIDAR can be analysed in real-time to detect anomalous objects and movements; using GNN, anomalous movements and obstacles can be immediately detected from patterns in point cloud data, allowing vehicles, drones and other moving vehicles to automatically take avoidance actions This can be done, for example, by delivery drones. This can be applied, for example, to delivery drones that use GNN to analyse LIDAR data to detect obstacles that suddenly appear on their flight path and avoid them safely.
5. environmental mapping and navigation: GNNs are also useful in environmental mapping and navigation based on LIDAR data, where GNNs can be used to construct 3D maps and develop systems that move autonomously while responding to the changing environment in real time. Automated vehicles and robots can safely navigate by selecting the most appropriate path while responding to dynamic obstacles and terrain changes.
The advantages of applying GNNs to LIDAR include.
1. simultaneous learning of local and global structures: GNNs can simultaneously learn the characteristics of local point clouds and the overall relationships they comprise, thus enabling precise analysis that takes into account not only the characteristics of individual points, but also the interactions between them.
2. efficient processing of high-dimensional data: point cloud data generated by LIDAR consists of a very large number of points, which can be difficult to process with ordinary neural networks GNNs can process data efficiently using a graph structure, which allows them to handle huge data sets without scalable solution.
3. noise tolerance: although LIDAR data often contains noise, GNNs are able to extract important features and make appropriate decisions even from noisy data, thereby minimising the effect of noise on the analysis.
The combination of LIDAR and GNN has been widely applied in the fields of automated driving, drones, robotics, smart cities and AR/VR. In the future, further integration with generative AI is expected to further improve the understanding and prediction accuracy of 3D environments and open up more diverse application fields.
Simple implementation example
A simple example of Python code for scene classification is shown as a combined implementation of LiDAR and GNN. Here, the framework is built by treating LiDAR data as a point cloud and inputting it to a GNN for scene classification.
1. library installation: run the following code to install the necessary libraries.
pip install torch
pip install torch_geometric
pip install numpy2. sample data preparation: LiDAR data is represented as 3D point cloud data. Here, a simple dataset is used, which contains position information (x, y, z) and category information (class of scene) for each point.
The following code generates random point cloud data.
import torch
import numpy as np
from torch_geometric.data import Data
# Randomly generated LiDAR data (100 points)
num_points = 100
points = np.random.rand(num_points, 3) # x, y, z coordinates.
labels = np.random.randint(0, 2, num_points) # 2 scene categories: 0 or 1
# 点群をPyTorch Geometricのデータ形式に変換
x = torch.tensor(points, dtype=torch.float) # Coordinates of the point cloud
y = torch.tensor(labels, dtype=torch.long) # label information
data = Data(x=x, y=y)3. define the GNN model: define the GNN model. Here, a simple model is built using a Graph Convolutional Network (GCN) to learn the features of the point cloud data.
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class PointGNN(torch.nn.Module):
def __init__(self):
super(PointGNN, self).__init__()
self.conv1 = GCNConv(3, 16) # Input is 3-dimensional in x, y, z
self.conv2 = GCNConv(16, 32)
self.conv3 = GCNConv(32, 2) # 2 classes of output (scene classification)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = self.conv2(x, edge_index)
x = F.relu(x)
x = self.conv3(x, edge_index)
return F.log_softmax(x, dim=1)4. generating edge information for graphs: GNNs need edge (connectivity) information and here edges are generated using the k-nearest neighbour method.
from torch_geometric.nn import knn_graph
# Create k-nearest neighbour graphs.
data.edge_index = knn_graph(data.x, k=5) # Graph generation in the neighbourhood of k=55. training and evaluating the model: finally, the model is trained and evaluated on test data.
from torch.optim import Adam
# Split data into training and testing.
train_data = data[:80] # Training data for the first 80 points.
test_data = data[80:] # Test data for the remaining 20 points.
# Initialisation of the model
model = PointGNN()
optimizer = Adam(model.parameters(), lr=0.01)
# training loop
model.train()
for epoch in range(100): # Learning at 100 epochs.
optimizer.zero_grad()
out = model(train_data)
loss = F.nll_loss(out[train_data.y], train_data.y) # Loss Calculation
loss.backward()
optimizer.step()
print(f'Epoch {epoch}, Loss: {loss.item()}')
# model evaluation
model.eval()
pred = model(test_data).argmax(dim=1)
accuracy = (pred == test_data.y).sum() / test_data.y.size(0)
print(f'Test Accuracy: {accuracy:.4f}')reference book
Reference books are described below.
Reference books on LiDAR and point cloud processing.
1. ‘LiDAR Remote Sensing and Applications’.
Author(s): Pinliang Dong, Qi Chen
Abstract: Covers the basics and applications of LiDAR technology, including practical examples on 3D modelling and environmental measurement, and provides detailed explanations on point cloud data handling and data analysis.
2. ‘Point Cloud Data Processing’.
Author(s): Hui Liu, Liang Cheng
Abstract: The book details data processing and analysis methods for point cloud data, from LiDAR data pre-processing to analysis using machine learning, and is particularly useful for projects involving implementation.
3. ‘3D Point Cloud Analysis’.
Author: Ruwen Schnabel
Abstract: Covers a wide range of topics related to the processing, analysis and application of point cloud data, including how LiDAR data can be utilised in models such as GNNs and a chapter on 3D scene understanding.
Reference books on graph neural networks (GNNs).
1. ‘Graph Representation Learning’.
Author: William L. Hamilton
Abstract: Describes the fundamentals of GNNs and how to design them for practical applications; although it does not directly address their use with LiDAR data, it is suitable for learning the basics of understanding GNNs.
2. ‘Deep Learning on Graphs’.
Author(s): Yao Ma, Jiliang Tang
Abstract: This book summarises the theory and practice of deep learning techniques on graph data. It includes state-of-the-art techniques such as graph convolutional networks and transformers and helps to get ideas for applying GNNs to point cloud data such as LiDAR.
3. ‘Graph Neural Networks: Foundations, Frontiers, and Applications’.
Author(s): Lingfei Wu, Peng Cui, Jian Pei
Abstract: This reference book summarises the latest research on graph neural networks and covers the application areas; it will be useful for scene understanding and object detection using GNNs, as well as for combining them with LiDAR data.
Other useful reference books for LiDAR and GNN integration
1. ‘Deep Learning for 3D Point Clouds’.
Author: Mohsen Ali
Abstract: Focuses on deep learning techniques for 3D point cloud data and includes practical examples of scene analysis utilising LiDAR data; also describes methods such as GNN and PointNet; includes a discussion of the use of LiDAR data for scene analysis and the use of LiDAR data for scene analysis; also includes a discussion of the use of LiDAR data for scene analysis and the use of LiDAR data for scene analysis.
2. ‘Geometric Deep Learning: grids, groups, graphs, geodesics, and Gauges’.
Author(s): Michael Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst
Abstract: The book focuses on geometric deep learning, with a particular focus on approaches related to graph data and 3D data.
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