Lidar Navigation Research Articles

果园环境中巡检机器人的 SLAM 和路径规划方法研究

Orchard robots play a crucial role in agricultural production. Autonomous navigation serves as the foundation for orchard robots and eco-unmanned farms. Accurate sensing and localization are prerequisites for achieving autonomous navigation. However, current vision-based navigation solutions are sensitive to environmental factors, such as light, weather, and background, which can affect positioning accuracy. Therefore, they are unsuitable for outdoor navigation applications. LIDAR provides accurate distance measurements and is suitable for a wide range of environments. Its immunity to interference is not affected by light, colour, weather, or other factors, making it suitable for low objects and complex orchard scenes. Therefore, LiDAR navigation is more suitable for orchard environments. In complex orchard environments, tree branches and foliage can cause Global Positioning System (GNSS) accuracy to degrade, resulting in signal loss. Therefore, the major challenge that needs to be addressed is generating navigation paths and locating the position of orchard robots. In this paper, an improved method for Simultaneous Localization and Mapping (SLAM) and A-star algorithm is proposed. The SLAM and path planning method designed in this study effectively solves the problems of insufficient smoothness and large curvature fluctuation of the path planned in the complex orchard environment, and improves the detection efficiency of the robot. The experimental results indicate that the method can consistently and accurately fulfil the robot's detection needs in intricate orchard environments.

Аналіз стану дослідження проблеми визначення параметрів відносного положення об'єктів орбітального сервісу

Recently considerable attention has been paid to the problem of estimating the pose of an on-orbit service object. Determining the pose at a close distance still remains an open line of research, especially for non-cooperative objects (targets) of on-orbit service. The goal of this work is to overview the state of the art in the problem of determining the relative motion parameters of on-orbit service objects with emphasis on close proximity operations with non-cooperative and unknown targets. The method employed is the analysis of publications devoted to this problem over the last decade. The analysis showed the following. Determining the pose of a non-cooperative orbital object using video systems is a classical approach due to the advantages of light weight and low power consumption. Video camera based pose estimation algorithms usually require prior knowledge of the target features. The main methods of pose estimation still involve approaches based on the recognition and correspondence of image features for consecutive frames or with a target model. Another major approach to pose determination is lidar navigation, where the recognition and correspondence of features based on lidar-derived target surface point clouds are also common methods. Recently, a trend has emerged towards the development of non-feature methods for target pose determination, including unknown targets. The three-dimensional nature of lidar point cloud data is favorable for target pose estimation without any target model. As to the applicability of target pose estimation methods to an unknown target, the implementation of the obvious approach based on constructing a three-dimensional model of the target by processing a series of its images prior to estimating its spatial motion takes a lot of time, which is critical in close proximity operations. The trend in target pose estimation is the development of methods for simultaneous estimation of the pose and shape of an unknown object. In general, the case of an unknown object has not yet been fully investigated.

A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures

With its fast and accurate position and attitude estimation, the feature-based lidar-inertial odometer is widely used for UAV navigation in GNSS-denied environments. However, the existing algorithms cannot accurately extract the required feature points in the spatial grid structure, resulting in reduced positioning accuracy. To solve this problem, we propose a lidar-inertial navigation system based on grid and shell features in the environment. In this paper, an algorithm for extracting features of the grid and shell is proposed. The extracted features are used to complete the pose (position and orientation) calculation based on the assumption of local collinearity and coplanarity. Compared with the existing lidar navigation system in practical application scenarios, the proposed navigation system can achieve fast and accurate pose estimation of UAV in a GNSS-denied environment full of spatial grid structures.

A Global ArUco-Based Lidar Navigation System for UAV Navigation in GNSS-Denied Environments

With the continuous expansion of the application field of UAV intelligent systems to GNSS-denied environments, the existing navigation system can hardly meet low cost, high precision, and high robustness in such conditions. Most navigation systems used in GNSS-denied environments give up the connection between the map frame and the actual world frame, making them impossible to apply in practice. Therefore, this paper proposes a Lidar navigation system based on global ArUco, which is widely used in large-scale known GNSS-denied scenarios for UAVs. The system jointly optimizes the Lidar, inertial measurement unit, and global ArUco information by factor graph and outputs the pose in the real-world frame. The system includes a method to update the global ArUco confidence with sampling, improving accuracy while using the pose solved from the global ArUco. The system uses the global ArUco to maintain navigation when Lidar is degraded. The system also has a loop closure determination part based on ArUco, which reduces the consumption of computing resources. The navigation system has been tested in the dry coal shed of a thermal power plant using a UAV platform. Experiments demonstrate that the system can achieve global, accurate, and robust pose estimation in large-scale, complex GNSS-denied environments.

FAST-LIO2: Fast Direct LiDAR-Inertial Odometry

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and, hence, increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adaptable to emerging LiDARs of different scanning patterns; the second main novelty is maintaining a map by an incremental k-dimensional (k-d) tree data structure, incremental k-d tree ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> ), that enables incremental updates (i.e., point insertion and delete) and dynamic rebalancing. Compared with existing dynamic data structures (octree, R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\ast$</tex-math></inline-formula> -tree, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nanoflann</i> k-d tree), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> achieves superior overall performance while naturally supports downsampling on the tree. We conduct an exhaustive benchmark comparison in 19 sequences from a variety of open LiDAR datasets. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems. Various real-world experiments on solid-state LiDARs with small field of view are also conducted. Overall, FAST-LIO2 is computationally efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multiline spinning and solid-state LiDARs, unmanned aerial vehicle (UAV) and handheld platforms, and Intel- and ARM-based processors), while still achieving a higher accuracy than existing methods. Our implementation of the system FAST-LIO2 and the data structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ikd-Tree</i> are both open-sourced on Github.

HIGH-DEFINITION POINT CLOUD MAP-BASED 3D LiDAR-IMU CALIBRATION FOR SELF-DRIVING APPLICATIONS

Abstract. The multi-sensor fusion scheme has become more and more popular these days with its great potential to estimate reliable navigation information for the modern development in automated driving system (ADS) and mobile mapping systems (MMS). Since these systems are combined with numerous navigation sensors, thus their geometric relationship should be precisely known. This study focuses on practical aspects when calibrating LiDAR-IMU mounting parameters (lever-arms and bore-sight angles) in land-based MMS. This calibration model is based on expressing the mounting parameters within the direct georeferencing equation for each epoch time and conditioning a set of INS/GNSS and LiDAR navigation solutions to lie on it. There is no need for a required information about the planar features in the calibration field as part of the unknowns. Such conditions are only benefitable in the residential area where the presence of sufficient planes in form of building is abundant. We present an approach for recovery the mounting parameters by conditioning the high-definition (HD) point cloud map-based LiDAR information and INS/GNSS navigation solutions through the least-squares solutions. The presented results and discussion mainly focus on practical examples with data from land-based MMS. Preliminary results indicate that correct calibration parameters are not only capable to improve the performance of point cloud georeferencing but also dramatically provide reliable performance evaluation of navigation estimation. Moreover, these findings show that the studied method is not only applicable in the featureless environment but also in its practicality to the self-driving applications.

A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation.

In this paper, we develop new methods to assess safety risks of an integrated GNSS/LiDAR navigation system for highly automated vehicle (HAV) applications. LiDAR navigation requires feature extraction (FE) and data association (DA). In prior work, we established an FE and DA risk prediction algorithm assuming that the set of extracted features matched the set of mapped landmarks. This paper addresses these limiting assumptions by incorporating a Kalman filter innovation-based test to detect unwanted object (UO). UO include unmapped, moving, and wrongly excluded landmarks. An integrity risk bound is derived to account for the risk of not detecting UO. Direct simulations and preliminary testing help quantify the impact on integrity and continuity of UO monitoring in an example GNSS/LiDAR implementation.

Potentialities of laser systems for remote sensing of the atmosphere at a wide variability of optical and physical characteristics: dimensionless-parametric modelling

Within the framework of generalisation of different approaches to the modelling of atmospheric lidars, the methodology capabilities for dimensionless-parametric analysis are expanded. The developed approach simplifies the analysis of the signal-to-noise ratio and potential capabilities of existing and newly developed monitoring systems with a wide variability of atmospheric and optical conditions and a great variety of modern lidars. Its applicability to the problems of remote atmospheric sensing, environmental monitoring and lidar navigation in providing the eye safety, noise immunity and reliability is discussed.