With the continuous advancement of modern technology, the application range of UAVs is becoming increasingly widespread. In recent years, UAVs have been appearing more frequently in people’s sight. During UAV flights, surveying tasks need to be completed in preparation for future navigation inspections. The SLAM process in the UAV system requires high real-time performance, especially in terms of CPU information processing speed, which has become the main research direction at present. Based on the type of sensor used, SLAM systems can be categorized as visual SLAM, lidar SLAM, and multi-sensor fusion SLAM.

The camera offers abundant visual information at a low cost and in a compact form factor, enabling robot localization and navigation. However, purely visual SLAM performance often deteriorates in low-textured environments. To address this issue, researchers have combined points with other geometric entities such as lines as lines [1] or planes [2]. In human-made environments, a pose-graph optimization strategy can be used to take advantage of structural constraints such as parallelism or orthogonality of walls. Another well-known approach for reducing rotation drift is to adopt the Manhattan World (MW) assumption [3]. However, most of the methods discussed above use RGB-D cameras in human-made environments, which may not be universally applicable. Moreover, the accuracy of the system depends heavily on the estimation of the ground plane and Manhattan Axes (MA). Recently, with the development of deep learning, a combined system of SLAM and a Convolutional Neural Network (CNN) has emerged. In [4], a table retrieval method is proposed for data association and loop closure using semantic information in a dynamic environment. Each landmark is associated with its own semantic and location information to improve the accuracy of the system.

To address the limitations of purely visual SLAM, the fusion of vision and IMU data have become mainstream. The IMU is primarily used to measure acceleration and rotational motion, providing high-frequency and outlier-free inertial measurements. However, the IMU’s long-term operation may result in significant accumulated drift, which necessitates the initialization of all IMU parameters and real-time optimization in the later stages. This is critical for visual–inertial odometry (VIO) and visual–inertial SLAM systems, and researchers are actively seeking ways to quickly complete the initialization of the IMU and suppress its noise and bias. Currently, the initialization methods of VIO systems can be broadly categorized into two main approaches: loosely coupled [5,6] and tightly coupled [7]. The loosely coupled approach involves separate initialization processes for the IMU and the camera, followed by minimizing the distance between their poses. In VINS-Mono [5], keyframes and map points are initially obtained using visual odometry, and IMU parameters are optimized through aligning the IMU pre-integrated rotation with visual measurements by covariance propagation of the error term. However, this approach estimates the velocity as an unknown variable and overlooks the accelerometer bias, leading to incomplete initialization information. On the other hand, ORB-SLAM3 [6] is a visual–inertial tightly coupled system that employs MAP estimation to estimate scale, gravity direction, biases, and velocity during IMU initialization, while the tightly coupled approach directly establishes constraints between the camera and IMU during the initialization process to optimize various parameters. OpenVINS [7] is a tightly coupled initialization approach that leverages camera poses to establish visual constraints, enabling the estimation of initial velocity, gravity, and three-dimensional coordinates of feature points. Subsequently, multiple-frame velocity and position relationships are obtained through first-order and second-order integration, respectively. BASALT [8] employs a two-level SLAM system that optimizes the noise and bias of the IMU in both stages. In contrast to other systems, it does not directly utilize the pre-integrated IMU measurements in the mapping stage. Instead, it extracts short-term visual–inertial tracking information from the marginalized information of the VI-odometry stage. This approach not only reduces the dimensionality of the global optimization problem but also enhances the accuracy of the optimization results. GVINS [9] employs a coarse-to-fine approach to initialize GNSS visual–inertial states using MAP estimation and integrates their raw measurements within a probabilistic framework. It is capable of providing drift-free 6-DoF global pose estimation in complex environments where GNSS signals may be obstructed or entirely unavailable.

One of the challenges in developing SLAM systems is ensuring algorithm robustness and real-time performance while working with limited computing resources, such as cheap and low-performance processors. This is especially important for battery-powered robots, where computational efficiency is crucial for extending the robot’s endurance. To address these challenges, researchers have proposed various approaches. For example, Ref. [10] uses a direct method to initialize the system and tracks non-keyframes for state estimation at the front-end. At the back-end, sliding window and marginalization are adopted to limit the number of keyframes and perform nonlinear optimization. Similarly, FastORB-SLAM [11] tracks keypoints between incoming frames without computing descriptors, exploiting motion smoothness and constraints on epipolar geometry to refine the correspondence. ORB-SLAM2S [12] includes a lightweight front-end that uses a sparse optical flow method for non-keyframes and descriptors, achieving faster speed performance compared to ORB-SLAM2. However, these methods often replace ORB features with direct methods and optical flow methods to track feature points, which can lead to reduced system accuracy if feature points are not extracted properly.

Our laboratory is developing an inspection robot that relies on the Robot Operating System (ROS) and object detection algorithms to achieve mapping and monitoring. Endurance and real-time tracking are key factors for inspection robots. Therefore, we propose a lightweight stereo-inertial SLAM based on nonlinear optimization and feature tracking, which achieves fast tracking, better robustness, and a lower CPU load. The overall system architecture is shown in Figure 1. Our three main contributions focus on speeding up tracking, reducing CPU consumption, and maintaining system accuracy. The main contributions are summarized as follows:

• A coarse-to-fine optimization approach. Coarse optimization is for faster IMU initialization to replace the constant velocity model and speed up the tracking process, while fine optimization ensures localization accuracy.
• A novel visual–inertial pose graph as an observer decides which variables need to be optimized to prevent over-optimization.
• Fusion of IMU data with loop closure to further reduce CPU load.

2. A Lightweight UAV System