https://ira.lib.polyu.edu.hk/bitstream/10397/80398/2/Zhang_Intelligent_GNSS_INS.pdf

Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system

Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m × 32 m test sites that were characterized as sparse ( n = 42 trees) and obstructed ( n = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories.

Under-canopy UAV laser scanning for accurate forest field measurements

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.

/pdf/vehicle-position-correction-a-vehicular-blockchain-networks-6ieza3gbkz.pdf

Vehicle Position Correction: A Vehicular Blockchain Networks-Based GPS Error Sharing Framework

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.

Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge

Accurate vehicle self-localization is significant for autonomous driving. The localization techniques based on Global Navigation Satellite System (GNSS) cannot achieve the required accuracy in urban canyons. On the other hand, simultaneous localization and mapping (SLAM) methods suffer from the error accumulation problem. State-of-the-art localization approaches adopt 3D Light Detection and Ranging (Lidar) to observe the surrounding environment and match the observation with a priori known 3D point cloud map for estimating the position of the vehicle within the map. However, storing the massive point cloud needs immense storage on the vehicle, or it should be stored on servers, which makes the simultaneous downloading of the map by multiple vehicles another challenge. In this study, rather than employing the point cloud directly as the prior map, we focus on the abstract map of buildings, which is easy to extract, and at the same time apparently observable by Lidar. More especially, we proposed vehicle localization methods based on two different abstract map formats representing urban areas. The first format is the multilayer 2D vector map of building footprints, which represents the building boundaries using vectors (lines). The second format is the planar surface map of buildings and ground. These map formats share the same idea that the uncertainty (deviation) of each vector or planar surface is calculated and included in the map. Later in the localization phase, the observed data from Lidar is matched with the abstract map to obtain the precise location of the vehicle. Experiments conducted in a dense urban area of Tokyo show that even though we significantly shrank the map size, we could preserve the mean error of the localization.

Autonomous vehicle self-localization based on abstract map and multi-channel LiDAR in urban area

Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT) The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Recently, autonomous vehicle’s self-localization based on the matching of laser scanner data to the high definition (HD) map become more popular due to the availability of HD map and price down of light detection and ranging technologies. Many types of research have been done to achieve locally and globally accurate HD map for accurate localization. However, the global accuracy of the map does not guarantee accurate self-localization within the map. To achieve accurate self-localization, the map should satisfy some requirements. In this paper, the focus is made on the map, as one of the high potential sources of error in localization. By investigating the erroneous scenarios in the map and comparing their characteristics, we introduced four criteria for the self-localization ability of the map. These criteria are feature sufficiency, layout, local similarity, and representation quality of the map. Then, in order to quantify these criteria, we introduce several factors for each criterion. Unlike evaluation criteria which are defined regardless of the map formats, factors are defined based on normal distribution map which is a map format of normal distribution transformation scan-matching. These factors are calculated for each position in the map, based on the map features within its local vicinity. By conducting the experiments in Shinjuku, Japan, we have evaluated these factors in a different part of the map with different scenarios by comparing them with the self-localization error.

Factors to Evaluate Capability of Map for Vehicle Localization

Accurate vehicle self-localization is one of the crucial requirements of Autonomous vehicles. Since GPS-based localization techniques cannot achieve required accuracy in urban canyons, recently LiDAR-based (Light Detection and ranging) localization techniques gained a focus due to its accuracy. One of the challenges of LiDAR-based map matching methods is a size of the map. This paper proposes a new structure of map which is a multilayer 2D vector map and localization methods based on multi-channel LiDAR. Proposed map is extremely small in size comparing to 3D point cloud maps while preserving the localization accuracy. As this 2D map is generated by accumulating different layers of buildings, it has less uncertainty. Further, this map provides more features for map matching comparing to the conventional 2D maps and as a result, the accuracy of localization is improved. On the other hand, vector structure of the map bring more precise NDT (normal distribution transform) representation and as a result, more accurate matching. Experimental results show that proposed method outperform the conventional 2D map matching techniques in terms of accuracy.

Autonomous vehicle self-localization based on multilayer 2D vector map and multi-channel LiDAR

Autonomous vehicle self-positioning based on 3D light detection and ranging (Lidar) has become popular recently due to disadvantages of global navigation satellite system (GNSS) in urban areas. As LiDAR-based simultaneous localization and mapping (SLAM) methods suffer from error accumulation, state-of-the-art approaches match the point cloud data acquired by LiDAR to the priori known 3D point cloud map to obtain the position of the vehicle within the map. However, 3D point cloud map is very expensive to store and download as it contains an enormous amount of data even for a small area (around 300 million points per km2). In this study, rather than using 3D point cloud directly as a map, we focused on the planar surfaces which are mostly available in urban areas, easy to extract, and at the same time clearly observable by LiDAR. Therefore, in our proposed map, we extract the planar surfaces from the 3D point cloud and calculate its uncertainty (deviation) and store them as a prior map. Accordingly, in this map, we can abstract several thousands of points by only one plane. As a result, we can extremely shrink the map size (25 million points to around 1000 planes). Later in the localization phase, we reconstruct Gaussian mixture model for each planar surface based on previously stored deviation, and match LiDAR data to it to obtain the precise location of the vehicle. Experiments conducted in one of the urban areas of Tokyo show that even though we extremely shrank the map size, we could preserve the mean error of the localization less than 43cm comparing to other point cloud based methods.

Ehsan Javanmardi

Papers

Autonomous vehicle self-localization based on abstract map and multi-channel LiDAR in urban area

Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

Factors to Evaluate Capability of Map for Vehicle Localization

Autonomous vehicle self-localization based on multilayer 2D vector map and multi-channel LiDAR

Autonomous vehicle self-localization based on probabilistic planar surface map and multi-channel LiDAR in urban area