scholarly journals A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars

2021 ◽  
Vol 13 (10) ◽  
pp. 2015
Author(s):  
Yusheng Wang ◽  
Yidong Lou ◽  
Yi Zhang ◽  
Weiwei Song ◽  
Fei Huang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Calibration Procedure ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Scan Registration ◽  
Extraction Processes ◽  
Large Motion ◽  
Map Alignment ◽  
In Virtue Of ◽  
Fusion Framework

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy.

scholarly journals Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3928 ◽  
Author(s):  
Weisong Wen ◽  
Li-Ta Hsu ◽  
Guohao Zhang
Keyword(s):  
New York ◽  
Hong Kong ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Tall Buildings ◽  
Point Clouds ◽  
Reliability Estimation ◽  
Traffic Condition ◽  
Localization And Mapping ◽  
Graph Slam

Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.

scholarly journals Composite clustering normal distribution transform algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091214
Author(s):  
Tian Liu ◽  
Jiongzhi Zheng ◽  
Zhenting Wang ◽  
Zhengdong Huang ◽  
Yongfu Chen
Keyword(s):  
Normal Distribution ◽  
Experimental Result ◽  
Geometric Feature ◽  
Matching Accuracy ◽  
Localization And Mapping ◽  
Density Based Clustering ◽  
Scan Registration ◽  
Novel Method ◽  
Value Decomposition

Scan registration is a fundamental step for the simultaneous localization and mapping of mobile robot. The accuracy of scan registration is critical for the quality of mapping and the accuracy of robot navigation. During all of the scan registration methods, normal distribution transform is an efficient and wild-using one. But normal distribution transform will lead to the unreasonable interruption when splitting the grid and can’t express the points’ local geometric feature by prefixed grid. In this article, we propose a novel method, composite clustering normal distribution transform, which comprises the density-based clustering and k-means clustering to aggregate the points with similar local distributing feature. It takes singular value decomposition to judge the suitable degree of one cluster for further division. Meanwhile, to avoid the radiating phenomenon of LIDAR in measuring the points’ distance, we propose a method based on trigonometric to measure the internal distance. The clustering method in composite clustering normal distribution transform could ensure the expression of LIDAR’s local distribution and matching accuracy. The experimental result demonstrates that our method is more accurate and more stable than the normal distribution transform and iterative closest point methods.

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Amitava Chatterjee
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Extended Kalman Filtering ◽  
Extended Kalman Filters ◽  
De Algorithm ◽  
Localization And Mapping ◽  
Recent Method ◽  
Fuzzy Supervisor

SUMMARYThe present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

scholarly journals Hololens Used for Precise Position Tracking of the Third Party Devices - Autonomous Vehicles

2019 ◽  
Vol 21 (2) ◽  
pp. 18-23
Author(s):  
Jindrich Cyrus ◽  
David Krcmarik ◽  
Reza Moezzi ◽  
Jan Koci ◽  
Michal Petru
Keyword(s):  
Autonomous Vehicles ◽  
Complex Space ◽  
Autonomous Driving ◽  
Real Space ◽  
Third Party ◽  
Location Information ◽  
Position Tracking ◽  
Precise Position ◽  
The Third ◽  
Localization And Mapping

A completely new area of HoloLens usage is proposed. The Hololens is an augmented reality device, which provides the high precision location information. Such an information is normally used to accurately position holograms within the real space with respect to the viewer (user of HoloLens). The information is precise enough to use it for reporting the position for the purpose of autonomous driving. Several experiments have been executed in vast areas (20 m x 40 m) in order to find out the potential error coming from vibrations or other effects when moving the HoloLens. The results show that the technology can be used for spaces, which are previously known by the system - pre-scan of the space is needed. The big advantage of the system is its readiness for indoor positioning applications with no additional infrastructure needed, simultaneous localization and mapping, complex space mapping and reached precision. The disadvantage is mainly the costs.

scholarly journals IMPROVED REFERENCE KEY FRAME ALGORITHM

2019 ◽  
Vol IV-2/W5 ◽  
pp. 133-139 ◽  
Author(s):  
H. A. Mohamed ◽  
A. Moussa ◽  
M. M. Elhabiby ◽  
N. El-Sheimy
Keyword(s):  
Autonomous Vehicles ◽  
Previous History ◽  
External Source ◽  
Indoor Environments ◽  
Scan Matching ◽  
Detection Range ◽  
Key Frame ◽  
Matching Process ◽  
Map Construction ◽  
Localization And Mapping

<p><strong>Abstract.</strong> The autonomous vehicles, such as wheeled robots and drones, efficiently contribute in the search and rescue operations. Specially for indoor environments, these autonomous vehicles rely on simultaneous localization and mapping approach (SLAM) to construct a map for the unknown environment and simultaneously to estimate the vehicle’s position inside this map. The result of the scan matching process, which is a key step in many of SLAM approaches, has a fundamental role of the accuracy of the map construction. Typically, local and global scan matching approaches, that utilize laser scan rangefinder, suffer from accumulated errors as both approaches are sensitive to previous history. The reference key frame (RKF) algorithm reduces errors accumulation as it decreases the dependency on the accuracy of the previous history. However, the RKF algorithm still suffers; as most of the SLAM approaches, from scale shrinking problem during scanning corridors that exceed the maximum detection range of the laser scan rangefinder. The shrinking in long corridors comes from the unsuccessful estimation of the longitudinal movement from the implemented RKF algorithm and the unavailability of this information from external source as well. This paper proposes an improvement for the RKF algorithm. This is achieved by integrating the outcomes of the optical flow with the RKF algorithm using extended Kalman filter (EKF) to overcome the shrinking problem. The performance of the proposed algorithm is compared with the RKF, iterative closest point (ICP), and Hector SLAM in corridors that exceed the maximum detection range of the laser scan rangefinder.</p>

scholarly journals Improved UKF-SLAM with Lie Group Operation and Robust Feature Tracking for Motion Vehicles

2021 ◽  
Vol 2095 (1) ◽  
pp. 012036
Author(s):  
Xin Huang ◽  
Zuoxian Liang ◽  
Kai Zhang ◽  
Pingyuan Liu
Keyword(s):  
Lie Group ◽  
Autonomous Vehicles ◽  
Feature Tracking ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Measurement Models ◽  
Localization And Mapping ◽  
Tightly Coupled ◽  
Slam Algorithm ◽  
Extensive Experimental Data

Abstract This paper proposes an improved simultaneous localization and mapping (SLAM) algorithm based on tightly coupled camera images and IMU data, which provides accurate and robust localization for autonomous vehicles and unmanned aerial vehicles (UAV), especially for those in GPS-denied environments. Many research efforts have demonstrated the effectiveness of fusing camera images and inertial data with the Unscented Kalman filter (UKF), but there is still one tricky problem about the non-linearity of the kinematics of rotations. To address this issue, we propose a novel UKF-SLAM approach by rebuilding system and measurement models based on the Lie group and Lie algebra, which obtains state estimates with reasonably high accuracy. Besides, we also offer a new method to handle corner matching outliers, which only causes slightly additional computation costs but eliminates outliers and enhances corner tracking robustness. Results from extensive experimental data have validated the effectiveness of the proposed approach, and this method also achieves comparable precision to the state-of-art.

scholarly journals A Brief Survey on SLAM Methods in Autonomous Vehicle

2018 ◽  
Vol 7 (4.27) ◽  
pp. 38 ◽  
Author(s):  
Talha Takleh Omar Takleh ◽  
Nordin Abu Bakar ◽  
Shuzlina Abdul Rahman ◽  
Raseeda Hamzah ◽  
Zalilah Abd Aziz
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Simultaneous Localization And Mapping ◽  
General Concept ◽  
Research Community ◽  
Current Trends ◽  
Localization And Mapping ◽  
Pros And Cons ◽  
Self Driving Cars

The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semi-autonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today’s autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges. 

scholarly journals Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 161 ◽  
Author(s):  
Junqiao Zhao ◽  
Yewei Huang ◽  
Xudong He ◽  
Shaoming Zhang ◽  
Chen Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Indoor Localization ◽  
Autonomous Driving ◽  
Human Beings ◽  
Low Level ◽  
Localization And Mapping ◽  
Challenging Tasks ◽  
Parking Lot ◽  
Feature Based

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

Sign in / Sign up

Export Citation Format

Share Document