Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

PurposeIn large-scale environments and unstructured scenarios, the accuracy and robustness of traditional light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) algorithms are reduced, and the algorithms might even be completely ineffective. To overcome these problems, this study aims to propose a 3D LiDAR SLAM method for ground-based mobile robots, which uses a 3D LiDAR fusion inertial measurement unit (IMU) to establish an environment map and realize real-time localization.Design/methodology/approachFirst, we use a normal distributions transform (NDT) algorithm based on a local map with a corresponding motion prediction model for point cloud registration in the front-end. Next, point cloud features are tightly coupled with IMU angle constraints, ground constraints and gravity constraints for graph-based optimization in the back-end. Subsequently, the cumulative error is reduced by adding loop closure detection.FindingsThe algorithm is tested using a public data set containing indoor and outdoor scenarios. The results confirm that the proposed algorithm has high accuracy and robustness.Originality/valueTo improve the accuracy and robustness of SLAM, this method proposed in the paper introduced the NDT algorithm in the front-end and designed ground constraints and gravity constraints in the back-end. The proposed method has a satisfactory performance when applied to ground-based mobile robots in complex environments experiments.

<span lang="EN-US">In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.</span>

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

Airborne Light Detection and Ranging (LiDAR) topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM) generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE) comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW) spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.

We propose a method to improve the geo-referenced accuracy and self-consistency of bathymetric maps generated by Autonomous Underwater Vehicles (AUVs), where the navigation solution is prone to drift when GPS or other methods of absolute positioning are unavailable. This is accomplished using a non-feature based approach to Simultaneous Localization and Mapping (SLAM) that utilizes a 2D grid structure to represent the map and a Distributed Particle Filter to track the uncertainty in the vehicle state. Our method does not need to explicitly identify features in the surrounding environment or apply complicated matching algorithms to our bathymetry, as is commonly done when performing terrain aided navigation. In this work we demonstrate how a prior low-resolution map generated by a surface vessel in a standard gridded form can be readily integrated into our approach to bathymetric SLAM to additionally enforce consistency between the prior map and the AUV bathymetry. We illustrate the proposed approach using data from recent survey work undertaken off the coast of the Tasman Peninsula in South Eastern Tasmania, Australia. The results achieved by the Bathymetric distributed Particle SLAM (BPSLAM) filter are shown to improve the maps and trajectories when compared to dead reckoning fused with USBL observations.

Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

It is generally recognized that the widely utilized Global Navigation Satellite System (GNSS) signals may be severely challenged or even unavailable in enclosed or partially enclosed repetitive and ambiguous environments (e.g., offices, hotels, hospitals, airports, industrial warehouses, container seaports). With the surge of autonomous robots applications, a robust localization algorithm that is capable to accurately localize the robots within such challenging environments is a critical pre-requisite to enable functionalities such as mapping, navigation, intelligent services, and collaborative works. Current localization solutions in such environments can be classified into two main categories: infrastructure-based and infrastructure-free. For infrastructure-based solutions (e.g., Wi-Fi based, UWB based, RFID based, etc.), the costs for deploying and maintaining the infrastructures can be high, which is also neither scalable nor flexible. As for infrastructure-free localization, many of the approaches only rely on a single-modal sensor, such as Light Detection and Ranging (LiDAR), camera, or magnetometer. Moreover, methods such as simultaneous localization and mapping (SLAM) have much lower cost and can be easily adapted to almost any environment. Normally, SLAM-based approaches work well in ordinary environments with rich geometric features. However, aforementioned repetitive environments contain lots of similar and ambiguous settings with few distinct geometric features, thus making SLAM-based methods inaccurate or even fail. Over the past few years, the ambient magnetic field (MF) based positioning and localization algorithms have drawn significant attention. The MF has exhibited high distinctiveness and ubiquity at different location, which makes it a viable alternative to the GNSS signal for localization in the local environments. To take the aforementioned challenges and the advantages of MF into account, this thesis proposes a systematic multi-sensor fusion probabilistic localization framework for autonomous robots operating in repetitive environments.

Currently, 3D point clouds are obtained via LiDAR (Light Detection and Ranging) sensors to compute vegetation parameters to enhance agricultural operations. However, such a point cloud is intrinsically dependent on the GNSS (global navigation satellite system) antenna used to have absolute positioning of the sensor within the grove. Therefore, the error associated with the GNSS receiver is propagated to the LiDAR readings and, thus, to the crown or orchard parameters. In this work, we first describe the error propagation of GNSS over the laser scan measurements. Second, we present our proposal to overcome this effect based only on the LiDAR readings. Such a proposal uses a scan matching approach to reduce the error associated with the GNSS receiver. To accomplish such purpose, we fuse the information from the scan matching estimations with the GNSS measurements. In the experiments, we statistically analyze the dependence of the grove parameters extracted from the 3D point cloud -specifically crown surface area, crown volume, and crown porosity- to the localization error. We carried out 150 trials with positioning errors ranging from 0.01 meters (ground truth) to 2 meters. When using only GNSS as a localization system, the results showed that errors associated with the estimation of vegetation parameters increased more than 100 % when positioning error was equal or bigger than 1 meter. On the other hand, when our proposal was used as a localization system, the results showed that for the same case of 1 meter, the estimation of orchard parameters improved in 20 % overall. However, in lower positioning errors of the GNSS, the estimation of orchard parameters were improved up to 50% overall. These results suggest that our work could lead to better decisions in agricultural operations, which are based on foliar parameter measurements, without the use of external hardware.

A high-precision mapping is one of the essential requirements for safe autonomous driving because it is directly connected with the accuracy and safety of autonomous driving. Due to the development of 3D light detection and ranging (LiDAR) sensor technology, LiDAR is playing a key role to create the precise maps. Although many open-source-based LiDAR simultaneous localization and mapping (SLAM) algorithms have been developed, their accuracy in actual experiments is often not proven. To handle this issue, our paper aims to provide background materials for autonomous robots and experimental validations of open-source-based LiDAR SLAM. In the experiments, the comparison results with four state-of-the-art LiDAR mapping algorithms are described including LiDAR odometry and mapping(LOAM) and LiDAR inertial odometry mapping.

In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.