scholarly journals A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Rendong Wang ◽  
Youchun Xu ◽  
Miguel Angel Sotelo ◽  
Yulin Ma ◽  
Thompson Sarkodie-Gyan ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Initial Position ◽  
Urban Environments ◽  
Data Set ◽  
Registration Method ◽  
Position Errors ◽  
Initial Errors ◽  
High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

scholarly journals IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

2019 ◽  
Vol IV-4/W9 ◽  
pp. 35-42 ◽  
Author(s):  
Y. Dehbi ◽  
L. Lucks ◽  
J. Behmann ◽  
L. Klingbeil ◽  
L. Plümer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Support Vector ◽  
Mobile Mapping ◽  
General Applicability ◽  
Data Set ◽  
Building Models

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruihao Lin ◽  
Junzhe Xu ◽  
Jianhua Zhang
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Gps Data ◽  
Optimization Strategy ◽  
Data Set ◽  
Content Type ◽  
Localization And Mapping ◽  
Robot Positioning

Purpose Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map. Design/methodology/approach The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data. Findings On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines. Originality/value A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

scholarly journals Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

2020 ◽  
Vol 9 ◽  
Author(s):  
Jian Wu ◽  
Qingxiong Yang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Ground Plane ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Cloud Data ◽  
Dense Point ◽  
3D Lidar

In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly applied to sparse point clouds. This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks. The main contribution is the integration of the ground information which is used to group ground points far away from each other. Qualitative and quantitative experiments on two large-scale point cloud datasets show that the proposed method outperforms the current state-of-the-art.

scholarly journals Semantic segmentation–aided visual odometry for urban autonomous driving

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773566 ◽  
Author(s):  
Lifeng An ◽  
Xinyu Zhang ◽  
Hongbo Gao ◽  
Yuchao Liu
Keyword(s):  
Visual Cues ◽  
Dynamic Environment ◽  
Semantic Segmentation ◽  
Visual Odometry ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Data Sets ◽  
Dynamic Scenes ◽  
Data Set ◽  
Feature Based

Visual odometry plays an important role in urban autonomous driving cars. Feature-based visual odometry methods sample the candidates randomly from all available feature points, while alignment-based visual odometry methods take all pixels into account. These methods hold an assumption that quantitative majority of candidate visual cues could represent the truth of motions. But in real urban traffic scenes, this assumption could be broken by lots of dynamic traffic participants. Big trucks or buses may occupy the main image parts of a front-view monocular camera and result in wrong visual odometry estimation. Finding available visual cues that could represent real motion is the most important and hardest step for visual odometry in the dynamic environment. Semantic attributes of pixels could be considered as a more reasonable factor for candidate selection in that case. This article analyzed the availability of all visual cues with the help of pixel-level semantic information and proposed a new visual odometry method that combines feature-based and alignment-based visual odometry methods with one optimization pipeline. The proposed method was compared with three open-source visual odometry algorithms on Kitti benchmark data sets and our own data set. Experimental results confirmed that the new approach provided effective improvement both on accurate and robustness in the complex dynamic scenes.

scholarly journals A Dynamical Initialization Scheme for Tropical Cyclones under the Influence of Terrain

2018 ◽  
Vol 33 (3) ◽  
pp. 641-659 ◽  
Author(s):  
Hao-Yan Liu ◽  
Yuqing Wang ◽  
Jing Xu ◽  
Yihong Duan
Keyword(s):  
Tropical Cyclones ◽  
Large Scale ◽  
Inner Core ◽  
Initial Position ◽  
Position Errors ◽  
Initial Errors ◽  
Large Scale Analysis ◽  
Vortex Separation ◽  
Vortex Size ◽  
Initialization Scheme

Abstract This study extends an earlier dynamical initialization (DI) scheme for tropical cyclones (TCs) to situations under the influence of terrain. When any terrain lower than 1 km exists between 150 and 450 km from the TC center, topographic variables are defined and a filtering algorithm is used to remove noise due to the presence of terrain before the vortex separation is conducted. When any terrain higher than 1 km exists between 150 and 300 km from the TC center, or the TC center is within 150 km of land, a semi-idealized integration without the terrain is conducted to spin up an axisymmetric TC vortex before the inclusion of the terrain and the merging of the TC vortex with the large-scale analysis field. In addition, a procedure for the vortex size/intensity adjustment is introduced to reduce the initial errors before the forecast run. Two sets of hindcasts, one without (CTRL run) and one with the new DI scheme (DI run), are conducted for nine TCs affected by terrain over the western North Pacific in 2015. Results show that the new DI scheme largely reduces the initial position and intensity errors. The 72-h position errors and the intensity errors up to the 36-h forecasts are smaller in DI runs than in CTRL runs and smaller than those from the HWRF forecasts for the same TCs as well. The new DI scheme is also shown to produce the TC inner-core structure and rainbands more consistent with satellite and radar observations.

Dynamic multi-sensor platform for efficient 3D-digitalization of cities: part II

2021 ◽  
pp. 1-9
Author(s):  
Angel-Ivan Garcia-Moreno
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Data Set ◽  
Urban Scenes ◽  
3D Data ◽  
Sensor Platform ◽  
Laser Scanners ◽  
Potential Applications

Abstract The digitization of geographic environments, such as cities and archaeological sites, is of priority interest to the scientific community due to its potential applications. But there are still several issues to address. There are various digitization strategies, which include terrestrial/ airborne platforms and composed of various sensors, among the most common, cameras and laser scanners. A comprehensive methodology is presented to reconstruct urban environments using a mobile land platform. All the implemented stages are described, which includes the acquisition, processing, and correlation of the data delivered by a Velodyne HDL-64E scanner, a spherical camera, GPS, and inertial systems. The process to merge several point clouds to build a large-scale map is described, as well as the generation of surfaces. Being able to render large urban areas using a low density of points but without losing the details of the structures within the urban scenes. The proposal is evaluated using several metrics, for example, Coverage and Root-Mean-Square-Error (RSME). The results are compared against 3 methodologies reported in the literature. Obtaining better results in the 2D/3D data fusion process and the generation of surfaces. The described method has a low RMSE (0.79) compared to the other methods and a runtime of approximately 40 seconds to process each data set (point cloud, panoramic image, and inertial data). In general, the proposed methodology shows a more homogeneous density distribution without losing the details, that is, it conserves the spatial distribution of the points, but with fewer data.

scholarly journals SEMANTIC LABELLING OF ROAD FURNITURE IN MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 247-254 ◽  
Author(s):  
F. Li ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
3D Point Clouds ◽  
Promising Solution ◽  
Novel Method ◽  
Detailed Interpretation ◽  
Semantic Labelling

Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. Much research has been investigated on road furniture interpretation in both 2D images and 3D point clouds. Precise interpretation of road furniture in mobile laser scanning data still remains unexplored. In this paper, a novel method is proposed to interpret road furniture based on their logical relations and functionalities. Our work represents the most detailed interpretation of road furniture in mobile laser scanning data. 93.3 % of poles are correctly extracted and all of them are correctly recognised. 94.3 % of street light heads are detected and 76.9 % of them are correctly identified. Despite errors arising from the recognition of other components, our framework provides a promising solution to automatically map road furniture at a detailed level in urban environments.

scholarly journals Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

2021 ◽  
Vol 13 (24) ◽  
pp. 5066
Author(s):  
Mohammad Aldibaja ◽  
Naoki Suganuma
Keyword(s):  
Autonomous Vehicles ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Railway Bridges ◽  
The Road ◽  
Position Errors ◽  
Challenging Environment ◽  
Graph Slam ◽  
Unique Graph ◽  
Absolute Coordinate System

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.

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