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In this work, Voxel‐SLAM (simultaneous localization and mapping) is introduced: a complete, accurate, and versatile LiDAR (light detection and ranging) ‐inertial SLAM system consisting of five modules: initialization, odometry, local mapping (LM), loop closure (LC), and global mapping (GM), all employing the same map representation, an adaptive voxel map. The Voxel‐SLAM effectively utilizes short‐term, mid‐term, long‐term, and multimap data associations to achieve real‐time state estimation and high‐precision mapping. The odometry leverages short‐term data association and estimates current state with minimal latency. The LM, utilizing the mid‐term data association, designs an efficient LiDAR‐inertial bundle adjustment (BA) to refine the local map and states within a sliding window, which can run on an onboard computer in real time. The LC, exploiting the long‐term and multisession data association, can detect loops and support multisession mapping (up to five sessions in the experiments). To further capitalize these two data associations, the GM introduces an efficient global BA method and can even run on an onboard computer. Moreover, Voxel‐SLAM designs a robust initialization module to make the system start normally even under aggressive initial motion. In the presence of severe scene degeneracy or tracking loss, the system can automatically restart and relocalize to the previous tracking‐loss session when revisiting. :

Light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) has proven effective for robust perception in challenging environments. However, most existing LiDAR SLAM methods primarily rely on geometric features for pose estimation, often neglecting the intensity information of each point. This limitation often results in registration failures, especially in unstructured environments where geometric features are sparse or absent. To address this limitation, we propose IaNDT-SLAM, an intensity-augmented normal distribution transform (NDT)-based LiDAR-inertial SLAM system that integrates intensity information to enhance the robustness of both odometry and loop closure detection (LCD). In the odometry process, a dynamic geometric-intensity fusion model is developed to adaptively balance the contributions of geometric and intensity information during NDT registration. To enhance the robustness of LCD against viewpoint changes and dynamic objects, we design a novel global descriptor, the entropy-intensity scan context (EISC), which effectively captures environmental structure while employing correlation coefficients to quantify the similarity between descriptors. Extensive evaluations on public and self-collected datasets demonstrate that IaNDT-SLAM reduces the absolute translational error (ATE) by 60.5% and improves the maximum F1-score in LCD by 29.5%, outperforming state-of-the-art (SOTA) geometric-only and intensity-assisted LiDAR SLAM methods in both accuracy and robustness.

The ultra-wideband (UWB)-based real-time localization system (RTLS) is a promising technology for locating and tracking assets and personnel in real-time within a defined indoor environment since it provides high-ranging accuracy. However, its performance can be affected by the underlying antenna delays of UWB nodes, which act as a source of error during range estimations. Usually, measurement of the antenna delays is performed separately as a dedicated standalone procedure. Such an additional measurement procedure makes the UWB-based RTLS more tedious with manual interventions. Moreover, the air-time occupancy during the transmission and reception of signaling messages for range estimations between UWB node pairs also limits the serviceable capability of these networks. In this regard, we present a novel simultaneous ranging scheme that requires limited air-time occupancy during range estimations between UWB node pairs and also compensates for the error from the antenna delays. This paper provides a detailed mathematical modeling, system design, and implementation procedure of the proposed scheme. The effectiveness of the proposed scheme for locating a mobile node in an indoor environment is validated through experimental analysis. The results show that, compared to the state-of-the-art two-way ranging (TWR) method, the proposed scheme eliminates the requirement of dedicated standalone antenna delay measurement procedures of the nodes, increases air efficiency through the provision of simultaneous ranging, and provides relative root-mean-square errors (RMSEs) improvement for range and position estimations of approximately 54.52% and 39.96%, respectively.

Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.

PurposeThe light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.Design/methodology/approachA novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.FindingsThe proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.Originality/valueThe proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

Feature-based Simultaneous Localization and Mapping (SLAM) using 2D Light Detection and Ranging (LiDAR) in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data. The quality of these landmarks depends not only on the fitting algorithm, but also on how uncertainty is modeled and propagated from line estimates to derived corner features. Although the magnitude of LiDAR uncertainty has been widely studied, the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention. In particular, the scale ambiguity inherent to implicit line representations can degrade numerical conditioning and affect the stability of the propagated covariance estimates. This paper proposes a novel Weighted Conformal Total Least Squares (WCTLS) formulation for line extraction from 2D LiDAR data. Unlike conventional approaches, the proposed method enforces a geometrical normalization that removes scale ambiguity and improves the conditioning of the estimation problem. The method is compared with Unweighted Total Least Squares (UTLS) and Weighted Total Least Squares (WTLS) using real indoor datasets and repeated scans acquired from fixed sensor positions. The results show that all three formulations provide equivalent geometric corner locations, whereas the proposed WCTLS method consistently reduces the propagated uncertainty of the estimated corner coordinates. In addition, repeatability analysis over 100 scans per environment shows that WCTLS yields lower median corner ellipse areas and reduced dispersion across scans, without increasing computational complexity.

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

Autonomous robotic systems will be the future of planetary exploration missions. For autonomous robotic exploration, reliable pose estimation is required. This can be provided by cooperative radio localization, where radio signals are exchanged among the robots and other mission objects. Range and direction information is obtained by measuring the signal round-trip time (RTT) and direction-of-arrival (DoA), which enables position and orientation estimation of the robots. For the first time, we have demonstrated cooperative radio localization within a space-analogue exploration mission with two robotic rovers on the volcano Mt Etna. With this paper, we share our main lessons learned based on a thorough evaluation of measurement data. Thereby we identify estimation biases as the main error source. We then show how to estimate and compensate these biases during the mission by simultaneous localization and calibration (SLAC). We further investigate the impact of the radio signals reflected on the ground and on mission objects on the ranging accuracy. Then, we demonstrate the benefit of cooperation and the feasibility of single-link localization. In addition, we share tangible ranging and DoA estimation error models based on measurements in a realistic environment.

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.

This article evaluates the usefulness of mobile laser scanning technology (MLS) for determining selected tree and stand parameters. Usefulness was determined based on measurements taken with the Zeb-Horizon mobile scanner in four beech stands located in different parts of the country. For each stand, an XYZ point cloud was acquired and reference measurements of tree diameter at breast height (DBH) were taken. The data analysis involved processing the acquired MLS data and comparing it with the reference data. Preliminary tree location plans were prepared for the individual plots, and their accuracy was verified during subsequent fieldwork. The proportion of correctly identified trees ranged from 95.2 % to 100 %, with underestimation mainly affecting trees with small DBH (7–10 cm). The average differences between the mean DBH measured with the MLS technology and with a caliper were small and ranged from -4.0% to 0.3% (1 mm to 16 mm) for individual stands. Overestimation of DBH mainly affected thinner trees (up to 10 cm DBH), while underestimation affected thicker trees. Despite the high value of the coefficient of determination R2 (correlation of 0.9435 to 0.9945 for MLS data compared to reference data), only for one plot were the differences between these data in the Wilcoxon test non-significant. Differences in average DBH cross-sectional area calculated from the two methods ranged from 0.19% to -8.37% for individual stands and -2.86% on average for all trees. The greatest differences were found in a stand located on a mountain slope, where a large section had dense undergrowth. Such stand structure made it difficult to remotely detect the position of breast height on the point cloud. The results confirmed that the MLS technology can be applied in forest inventory. This method, due to the accuracy of the acquired data as well as the speed of its acquisition, can be an important alternative to traditional methods of measuring trees and stands.