Inertial Measurement Unit Preintegration Research Articles

Research on tightly coupled SLAM method based on LiDAR/IMU

Abstract Aiming at the problems of insufficient accuracy and serious computational time-consumption of mainstream laser odometry schemes, this paper proposes an innovative solution, where the front end is based on the idea of iterative Kalman filtering, which realizes the tight coupling of inertial measurement unit (IMU) and LiDAR to obtain high-precision position and attitude estimation. The back-end optimization employs a factor graph approach, incorporating laser odometry, IMU preintegration, and loop closure detection factors. In comparison with state-of-the-art laser SLAM algorithms, the SLAM algorithm proposed in this paper reduces the absolute position error of trajectories by 9.61%, 91.03%, and 94.89% compared to Fast-lio2, Lego-loam, and a-loam, respectively.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

BA-LIOM: tightly coupled laser-inertial odometry and mapping with bundle adjustment

AbstractWe design a scheme for laser-inertial odometry and mapping with bundle adjustment (BA-LIOM), which can greatly mitigate the problem of undesired ground warping due to sparsity of laser scans and significantly reduce odometry drift. Specifically, an Inertial measurement unit (IMU)-assisted adaptive voxel map initialization algorithm is proposed and elaborately integrated with the existing framework LIO-SAM, allowing for accurate registration in the beginning of the localization and mapping process. In addition, to accommodate to fast-moving and structure-less scenarios, we design a tightly coupled odometry, which jointly optimizes both the IMU preintegration constraints and scan matching with adaptive voxel maps. The voxels (edge and plane, respectively) are updated with BA optimization. And then the accurate mapping result is obtained by performing local BA. The proposed BA-LIOM is thoroughly assessed using datasets collected from multiple platforms over a variety of environments. Experimental results show the superiority of BA-LIOM over the state-of-the-art methods in robustness and precision, especially for large-scale scenarios. BA-LIOM improves the accuracy of localization by $61\%$ and $73\%$ on the buildings and lawn datasets, respectively, and has a $29\%$ accuracy improvement over LIO-SAM on the KITTI datasets. A supplementary video can be accessed at https://youtu.be/5l4ZFhTc2sw.

RESEARCH ON SENSOR FUSION-BASED CALIBRATION AND REAL-TIME POINT CLOUD MAPPING METHODS FOR LASER LIDAR AND IMU

Abstract. Simultaneous Localization and Mapping (SLAM), as one of the core technologies in autonomous driving, provides environment perception, decision-making, and path planning to ensure safe vehicle operation. This paper proposes a tightly-coupled fusion mapping method based on LiDAR and Inertial Measurement Unit (IMU). By introducing IMU, performing extrinsic calibration, and time synchronization with the LiDAR, the issue of mapping drift caused by inconsistent coordinate systems is addressed, leading to improved mapping accuracy. Furthermore, to compensate for IMU zero-bias errors, IMU preintegration is employed to calibrate the point cloud and optimize the initial values of the LiDAR odometry. A novel iVox voxel-based point cloud registration method is used to enhance registration efficiency without compromising mapping accuracy. Lastly, the Iterative Extended Kalman Filter (IEKF) is incorporated into the back-end state estimation to propagate and correct the estimated state using IMU data, enabling precise state estimation and map updates. This research is of significant importance in addressing the challenges of low mapping accuracy and real-time performance in single-sensor SLAM, providing an effective solution for environment perception and path planning in autonomous driving systems.

LIDAR-INERTIAL NAVIGATION BASED ON MAP AIDED DISTANCE CONSTRAINT AND FACTOR GRAPH OPTIMIZATION

Abstract. The simultaneous localization and mapping (SLAM) is one of the well-developed positioning technology that provides high accuracy and reliability positioning for automatic vehicles and robotics applications. Integrating Light Detection and Ranging (LiDAR) with an Inertial Measurement Unit (IMU) has emerged as a promising technique for achieving stable navigation results in dense urban environments, outperforming vision-based or pure Inertial Navigation System (INS) solutions. However, conventional LiDAR-Inertial SLAM systems often suffer from limited perception of surrounding geometric information, resulting in unexpected and accumulating errors. In this paper, we proposed a LiDAR-Inertial SLAM scheme that utilizes a prior structural information map which is generated from opensource OpenStreetMap (OSM). In contrast to conventional solutions of OSM-aided SLAM approaches, our method extracts the vectorized models of road and building and synthetically generates dense point maps for LiDAR registration. Specifically, a structural map processing module extracts the road models and building models from OSM and generates a structure information map (SIM) with dense point clouds. Secondly, a map aided distance (MD) constraint is calculated by registering selected keyframes and the prior SIM. Finally, a factor graph optimization (FGO) algorithm is involved to integrate the relative transformation obtained from LiDAR odometry, IMU pre-integration, and the map aided distance constraints. To evaluate the proposed LiDAR-based positioning accuracy, experimental evaluation is implemented in an opensource dataset collected in the urban canyon environments. Experimental results demonstrates that with the help of the proposed MD constraint, the LiDAR-based navigation solution can achieve accurate positioning, with a root mean square deviation (RSME) of 4.7 m.

LIDAR-INERTIAL LOCALIZATION WITH GROUND CONSTRAINT IN A POINT CLOUD MAP

Abstract. Real-time localization is a crucial task in various applications, such as automatic vehicles (AV), robotics, and smart city. This study proposes a framework for map-aided LiDAR-inertial localization, with the objective of accurately estimating the trajectory in a point clouds map. The proposed framework addresses the localization problem through a factor graph optimization (FGO), enabling the fusion of homogenous measurements for sensor fusion and designed absolute and relative constraints. Specifically, the framework estimates the light detection and ranging (LiDAR) odometry by leveraging inertial measurement unit (IMU) and registering corresponding featured points. To eliminate the accumulative error, this paper employs a ground plane distance and a map matching error to constraint the positioning error along the trajectory. Finally, local odometry and constraints are integrated using a FGO, including LiDAR odometry, IMU pre-integration, and ground constraints, map matching constraints, and loop closure. Experimental results were evaluated on an open-source dataset, UrbanNav, with an overall localization accuracy of 2.29 m (root mean square error, RMSE).

Rotating 3D laser mapping system for multi-rotor drones

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

An Enhanced Adaptable Factor Graph for Simultaneous Localization and Calibration in GNSS/IMU/Odometer Integration

The integration of global navigation satellite system (GNSS), inertial measurement unit (IMU) and odometer has been widely utilized in vehicular localization. Due to the advantage in accessing accurate state estimation, factor graph is commonly employed to achieve the integration of vehicular sensors. However, the fusion performance of traditional factor graph reduces when dealing with asynchronous and abnormal measurements, yielding compromised estimation results for localization and calibration. In this paper, an enhanced adaptable factor graph for simultaneous localization and calibration (SLAC) in GNSS/IMU/odometer integration is proposed. First, a novel factor graph framework for GNSS/IMU/odometer integration is designed. In view of the asynchronism among sensors, the variable nodes in factor graph are built according to odometer measurement. IMU pre-integration is then used to establish the relationship between asynchronous GNSS position and corresponding variable nodes to form residuals. To properly deal with the coupling noise in the proposed model, the expectation maximum (EM) algorithm is utilized to estimate the noise parameters based on the proposed model. Simulations and field tests are done to validate the proposed method. It shows that the proposed method has better estimation accuracy for localization and calibration compared with traditional methods. Meanwhile, the computational load of the proposed method is discussed.

A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.

Visual-Inertial-Laser SLAM Based on ORB-SLAM3

At present, visual simultaneous localization and mapping is a hot topic in the field of unmanned systems, which is popular among academic workers because of its advantages of accurate localization, low cost, large amount of information, and wide range of applications, but it still has some problems, including the camera’s vulnerability to the number of feature points and the noise impact of the inertial measurement unit during uniform linear motion. In response to the above problem this paper carries out the research on multi-sensor fusion localization algorithm, the main work is as follows: Based on ORB-SLAM3, a visual-inertial-laser SLAM algorithm is designed. The relative motion of laser location between image frames is obtained from the data of 2D Lidar and laser height sensor. The relative motion of inertial measurement unit between image frames is obtained from inertial measurement unit preintegration. Based on the method of factor graph optimization, the pose of image frame is optimized by reprojection of map point, relative motion increment of inertial measurement unit, and relative motion increment of laser location. The algorithm improves the localization accuracy by about 24.4% over the ORB-SLAM3 visual mode and about 22.6% over the ORB-SLAM3 visual-inertial mode on the data of the UAV physical platform.

RD-VIO: Relative-depth-aided visual-inertial odometry for autonomous underwater vehicles

Because of the autonomous underwater vehicles' (AUVs') slow motion and insufficient excitations, monocular visual-inertial odometry (VIO) systems for AUVs still have large-scale errors and a lengthy initialization period. In this paper, we present a relative-depth-aided monocular visual-inertial odometry (RD-VIO) system based on two innovative methods: Inertial Measurement Unit (IMU)-depth joint initialization and relative depth tightly coupled with visual-inertial. The RD-VIO performs reprojection bundle adjustment with dynamic weight factors for visual residuals. Depth measurements acquired by depth gauge or pressure sensor are compensated with reliable inter-image IMU pre-integration to account for different sensor frequencies. Also, we adopt an IMU-depth joint initialization using relative depth measurements and IMU excitations to estimate the scale and gravity direction together, which captures the complete AUV motion changes, improving scale estimation and decreasing initialization time. To reduce scale drift and improve global accuracy and consistency, a tight coupling of relative depth and visual-inertial in the RD-VIO is employed to optimize the AUV pose from depth measurements. Using two open-source datasets and our experiments from Sanya, we evaluated the performance of RD-VIO. The evaluation results show that the proposed system can achieve a more accurate trajectory than the state-of-the-art algorithms with less system initialization time and scale error.

Refined On-Manifold IMU Preintegration Theory for Factor Graph Optimization Based on Equivalent Rotation Vector

Multisource information fusion is a major area of interest within the field of unmanned technology. In comparison with utilizing the Kalman filter (KF)-based method, the factor graph optimization (FGO)-based method has recently been proposed as an innovative approach. However, the traditional FGO-based inertial measurement unit (IMU) preintegration process is designed for low-accuracy inertial sensors, which leads to insufficient utilization of information and a decrease in positioning accuracy, in the case of tactical-grade or higher accuracy IMUs. Therefore, this article refines the process on the manifold model and utilizes the equivalent rotation vector to accumulate IMU measurements. The main contribution of our work is the refined on-manifold IMU preintegration theory and the application of the multisample algorithm based on the equivalent rotation vector for obtaining more precise preintegrated IMU measurements. The refined on-manifold IMU preintegration theory is applied based on the FGO algorithm in the global navigation satellite system (GNSS)/inertial navigation system (INS) loosely integrated navigation system. The experiments based on simulated and real-world data were conducted and demonstrate that, compared to a high-precision extended KF (EKF) algorithm and optimization methods, the proposed method achieves more accuracy in a dynamic environment.

A Novel Plug-and-Play Factor Graph Method for Asynchronous Absolute/Relative Measurements Fusion in Multisensor Positioning

The presence of asynchronous absolute and relative measurements has posed a great challenge to the current multisensor positioning method in robotic systems. Although traditional factor graph methods possess a capability of plug-and-play, only a compromised performance can be obtained when dealing with an increasing number of asynchronous observations. In this article, a novel plug-and-play factor graph method for asynchronous absolute/relative measurements fusion in multisensor positioning is proposed. Different from traditional methods, a fixed-rate graph model is formed. The variable nodes in the graph are built at a fixed update rate to do the optimization, which is not affected by the arrival of measurements. Asynchronous absolute and relative measurements between two successive variable nodes are associated with corresponding variable nodes in the graph via the propagation of closed-form inertial measurement unit (IMU) preintegration. The simulations, datasets, and field tests are carried out to validate the proposed method. The results indicate that the closed-form IMU preintegration method has better dynamic adaptability in fusion system to formulate the association with asynchronous measurements. On this basis, the proposed method can integrate asynchronous measurements in a plug-and-play manner and achieve better performance compared to current methods. Meanwhile, the computational load can also be reduced.

A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation

Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection.

A Robust and Precise LiDAR-Inertial-GPS Odometry and Mapping Method for Large-Scale Environment

In this article, we propose a system that can fuse inertial measurement unit (IMU), light detection and ranging (LiDAR), and intermittent global positioning system (GPS) measurements to achieve high precision localization and mapping in large-scale environment. The iterated error-state Kalman filter is used to fuse IMU and LiDAR measurements to estimate relative motion information quickly. Based on the factor graph, LiDAR, GPS measurements, and loop closure are transformed into constraints for joint optimization to construct global map. A real-time GPS outlier detection method based on the IMU preintegration theory and residual chi-square test is designed. In addition, the use of robust kernel is supported to implicitly reduce the impact of undetected GPS outliers on the system. A LiDAR-based loop-closure detector is designed, which can search for a pair of point clouds with similar geometric information. Importantly, the performance of the proposed method is compared with state-of-the-art methods through a series of experiments. The experimental results show that the method is robust and accurate to complete the task of localization and mapping in large-scale environment.

Fusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping

Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have brought about an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D lidar, and an inertial measurement unit (IMU) sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D lidar mapping algorithm and improve the mapping effect. Lidar can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-degree of freedom pose. BVLI-SLAM uses graph optimization to fuse the data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real time. Outdoor experiments based on KITTI datasets and indoor experiments based on the trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer, and can solve the problem of positioning and plane mapping in indoor complex scenes.

A Spatiotemporal Calibration Algorithm for IMU-LiDAR Navigation System Based on Similarity of Motion Trajectories.

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems.

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel.

Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.

Semi-Direct Point-Line Visual Inertial Odometry for MAVs

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a novel semi-direct point-line visual inertial odometry (SDPL-VIO) has been proposed for MAVs. In the front-end, point and line features are introduced to enhance image constraints and increase environmental adaptability. At the same time, the semi-direct method combined with IMU pre-integration is used to complete motion estimation. This hybrid strategy combines the accuracy and loop closure detection performance of the feature-based method with the rapidity of the direct method, and tracks keyframes and non-keyframes, respectively. In the back-end, the sliding window mechanism is adopted to limit the computation, while the improved marginalization method is used to decompose the high-dimensional matrix corresponding to the cost function to reduce the computational complexity in the optimization process. The comparison results in the EuRoC datasets demonstrate that SDPL-VIO performs better than the other state-of-the-art visual inertial odometry (VIO) methods, especially in terms of accuracy and real-time performance.