INS-AIDED 3D LIDAR SEAMLESS MAPPING IN CHALLENGING ENVIRONMENT FOR FUTURE HIGH DEFINITION MAP

<p><strong>Abstract.</strong> With advances in computing and sensor technologies, onboard systems can deal with a large amount of data and achieve real-time process continuously and accurately. In order to further enhance the performance of positioning, high definition map (HD map) is one of the game changers for future autonomous driving. Instead of directly using Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) navigation solutions to conduct the Direct Geo-referencing (DG) and acquiring 3D mapping information, Simultaneous Localization and Mapping (SLAM) relies heavily on environmental features to derive the position and attitude as well as conducting the mapping at the same time. In this research, the new structure is proposed to integrate the INS/GNSS into LiDAR Odometry and Mapping (LOAM) algorithm and enhance the mapping performance. The first contribution is using the INS/GNSS to provide the short-term relative position information for the mapping process when the LiDAR odometry process is failed. The checking process is built to detect the divergence of LiDAR odometry process based on the residual from correspondences of features and innovation sequence of INS/GNSS. More importantly, by integrating with INS/GNSS, the whole global map is located in the standard global coordinate system (WGS84) which can be shared and employed easily and seamlessly. In this research, the designed land vehicle platform includes commercial INS/GNSS integrated product as a reference, relatively low-cost and lower grade INS system and Velodyne LiDAR with 16 laser channels, respectively. The field test is conducted from outdoor to the indoor underground parking lot and the final solution using the proposed method has a significant improvement as well as building a more accurate and reliable map for future use.</p>

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Determination of Cultivated Area, Field Boundary and Overlapping for A Plowing Operation Using ISO 11783 Communication and D-GNSS Position Data

Agriculture ◽

10.3390/agriculture9020038 ◽

2019 ◽

Vol 9 (2) ◽

pp. 38 ◽

Cited By ~ 6

Author(s):

Andreas Heiß ◽

Dimitrios Paraforos ◽

Hans Griepentrog

Keyword(s):

Crop Production ◽

Low Cost ◽

Satellite System ◽

Position Information ◽

Cost Differential ◽

Main Challenge ◽

Wide Range ◽

Position Data ◽

Machine Speed ◽

Global Navigation Satellite

Easily available and detailed area-related information is very valuable for the optimization of crop production processes in terms of, e.g., documentation and invoicing or detection of inefficiencies. The present study dealt with the development of algorithms to gain sophisticated information about different area-related parameters in a preferably automated way. Rear hitch position and wheel-based machine speed were recorded from ISO 11783 communication data during plowing with a mounted reversible moldboard plow. The data were georeferenced using the position information from a low-cost differential global navigation satellite system (D-GNSS) receiver. After the exclusion of non-work sequences from continuous data logs, single cultivated tracks were reconstructed, which represented as a whole the cultivated area of a field. Based on that, the boundary of the field and the included area were automatically detected with a slight overestimation of 1.4%. Different field parts were distinguished and single overlaps between the cultivated tracks were detected, which allowed a distinct assessment of the lateral and headland overlapping (2.05% and 3.96%, respectively). Incomplete information about the work state of the implement was identified as the main challenge to get precise results. With a few adaptions, the used methodology could be transferred to a wide range of mounted implements.

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SQUEEZEPOSENET: IMAGE BASED POSE REGRESSION WITH SMALL CONVOLUTIONAL NEURAL NETWORKS FOR REAL TIME UAS NAVIGATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w3-49-2017 ◽

2017 ◽

Vol IV-2/W3 ◽

pp. 49-57 ◽

Cited By ~ 3

Author(s):

M. S. Müller ◽

S. Urban ◽

B. Jutzi

Keyword(s):

Unmanned Aerial Vehicles ◽

Low Cost ◽

Satellite System ◽

Trajectory Estimation ◽

Localization And Mapping ◽

Wide Range ◽

Novel Method ◽

Computationally Expensive ◽

Global Navigation Satellite ◽

Local Image

The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

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Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map

Sensors ◽

10.3390/s20082166 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2166 ◽

Cited By ~ 1

Author(s):

Jeong Min Kang ◽

Tae Sung Yoon ◽

Euntai Kim ◽

Jin Bae Park

Keyword(s):

Ground Truth ◽

Autonomous Driving ◽

Satellite System ◽

Map Matching ◽

High Definition ◽

Driver Assistance Systems ◽

Vehicle Localization ◽

Reference Map ◽

Global Navigation Satellite ◽

Link Information

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

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TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽

10.3390/electronics10070782 ◽

2021 ◽

Vol 10 (7) ◽

pp. 782

Author(s):

Shuo Cao ◽

Honglei Qin ◽

Li Cong ◽

Yingtao Huang

Keyword(s):

Distribution System ◽

Large Scale ◽

Satellite System ◽

Time Of Arrival ◽

Military Operations ◽

Position Information ◽

Cooperative Navigation ◽

Navigation Algorithm ◽

Geometric Dilution Of Precision ◽

Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

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Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

Remote Sensing ◽

10.3390/rs13101981 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1981

Author(s):

Ruike Ren ◽

Hao Fu ◽

Hanzhang Xue ◽

Zhenping Sun ◽

Kai Ding ◽

...

Keyword(s):

3D Reconstruction ◽

High Precision ◽

Moving Objects ◽

Autonomous Driving ◽

Satellite System ◽

Factor Graph ◽

Mapping System ◽

Global Navigation ◽

Real World Datasets ◽

Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

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Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽

10.3390/s18103270 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3270 ◽

Cited By ~ 13

Author(s):

Hao Cai ◽

Zhaozheng Hu ◽

Gang Huang ◽

Dunyao Zhu ◽

Xiaocong Su

Keyword(s):

Kalman Filter ◽

Low Cost ◽

Kinematic Model ◽

Intelligent Vehicles ◽

Monocular Vision ◽

Measurement Unit ◽

High Definition ◽

Position Information ◽

Vehicle Localization ◽

Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

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Design of a Multiband Global Navigation Satellite System Radio Frequency Interference Monitoring Front-End with Synchronized Secondary Sensors

Sensors ◽

10.3390/s18082594 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2594

Author(s):

Aiden Morrison ◽

Nadezda Sokolova ◽

James Curran

Keyword(s):

Radio Frequency ◽

Global Navigation Satellite System ◽

Low Cost ◽

Satellite System ◽

Radio Frequency Interference ◽

Navigation Satellite ◽

Inertial Measurement ◽

Front End ◽

Global Navigation ◽

Global Navigation Satellite

This paper investigates the challenges of developing a multi-frequency radio frequency interference (RFI) monitoring and characterization system that is optimized for ease of deployment and operation as well as low per unit cost. To achieve this, we explore the design and development of a multiband global navigation satellite system (GNSS) front-end which is intrinsically capable of synchronizing side channel information from non-RF sensors, such as inertial measurement units and integrated power meters, to allow the simultaneous production of substantial amounts of sampled spectrum while also allowing low-cost, real-time monitoring and logging of detected RFI events. While the inertial measurement unit and barometer are not used in the RFI investigation discussed, the design features that provide for their precise synchronization with the RF sample stream are presented as design elements worth consideration. The designed system, referred to as Four Independent Tuners with Data-packing (FITWD), was utilized in a data collection campaign over multiple European and Scandinavian countries in support of the determination of the relative occurrence rates of L1/E1 and L5/E5a interference events and intensities where it proved itself a successful alternative to larger and more expensive commercial solutions. The dual conclusions reached were that it was possible to develop a compact low-cost, multi-channel radio frequency (RF) front-end that implicitly supported external data source synchronization, and that such monitoring systems or similar capabilities integrated within receivers are likely to be needed in the future due to the increasing occurrence rates of GNSS RFI events.

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Kinematic PPP Using Mixed GPS/Glonass Single-Frequency Observations

Artificial Satellites ◽

10.2478/arsa-2019-0008 ◽

2019 ◽

Vol 54 (3) ◽

pp. 97-112

Author(s):

Mostafa Hamed ◽

Ashraf Abdallah ◽

Ashraf Farah

Keyword(s):

Low Cost ◽

Satellite System ◽

Single Frequency ◽

Future Research ◽

Frequency Data ◽

Dual Frequency ◽

Trajectory Data ◽

Average Improvement ◽

Kinematic Ppp ◽

Global Navigation Satellite

Abstract Nowadays, Precise Point Positioning (PPP) is a very popular technique for Global Navigation Satellite System (GNSS) positioning. The advantage of PPP is its low cost as well as no distance limitation when compared with the differential technique. Single-frequency receivers have the advantage of cost effectiveness when compared with the expensive dual-frequency receivers, but the ionosphere error makes a difficulty to be completely mitigated. This research aims to assess the effect of using observations from both GPS and GLONASS constellations in comparison with GPS only for kinematic purposes using single-frequency observations. Six days of the year 2018 with single-frequency data for the Ethiopian IGS station named “ADIS” were processed epoch by epoch for 24 hours once with GPS-only observations and another with GPS/GLONASS observations. In addition to “ADIS” station, a kinematic track in the New Aswan City, Aswan, Egypt, has been observed using Leica GS15, geodetic type, dual-frequency, GPS/GLONASS GNSS receiver and single-frequency data have been processed. Net_Diff software was used for processing all the data. The results have been compared with a reference solution. Adding GLONASS satellites significantly improved the satellite number and Position Dilution Of Precision (PDOP) value and accordingly improved the accuracy of positioning. In the case of “ADIS” data, the 3D Root Mean Square Error (RMSE) ranged between 0.273 and 0.816 m for GPS only and improved to a range from 0.256 to 0.550 m for GPS/GLONASS for the 6 processed days. An average improvement ratio of 24%, 29%, 30%, and 29% in the east, north, height, and 3D position components, respectively, was achieved. For the kinematic trajectory, the 3D position RMSE improved from 0.733 m for GPS only to 0.638 m for GPS/GLONASS. The improvement ratios were 7%, 5%, 28%, and 13% in the east, north, height, and 3D position components, respectively, for the kinematic trajectory data. This opens the way to add observations from the other two constellations (Galileo and BeiDou) for more accuracy in future research.

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Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

Remote Sensing ◽

10.3390/rs13224525 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4525

Author(s):

Junjie Zhang ◽

Kourosh Khoshelham ◽

Amir Khodabandeh

Keyword(s):

Autonomous Driving ◽

Measurement Model ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

High Definition ◽

Satellite Systems ◽

The Road ◽

Gnss Receivers ◽

Vehicle Positioning ◽

Global Navigation Satellite

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.

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Aerial and Ground Robot Collaboration for Autonomous Mapping in Search and Rescue Missions

Drones ◽

10.3390/drones4040079 ◽

2020 ◽

Vol 4 (4) ◽

pp. 79

Author(s):

Dimitrios Chatziparaschis ◽

Michail G. Lagoudakis ◽

Panagiotis Partsinevelos

Keyword(s):

Humanoid Robot ◽

Satellite System ◽

Search And Rescue ◽

Localization Algorithm ◽

Localization And Mapping ◽

Planning Algorithm ◽

Crisis Scenarios ◽

Trained Neural Network ◽

Unknown Area ◽

Global Navigation Satellite

Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.

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