Initialization enhancements of ORB-SLAM3 under weak motion excitation

Visual inertial odometry (VIO) schemes based on tightly coupled optimization have become a research hotspot in the field of micro unmanned aerial vehicles and autonomous robots. However, the high-precision operation of VIO systems, such as VINS Mono and ORB-SLAM3, depends heavily on accurate initial state estimation, which involves estimating the visual scale factor, gravity direction, initial velocity, and Inertial Measurement Unit (IMU) gyroscope and accelerometer biases during the start-up phase. In practical application scenarios, such as the smooth low-speed start-up of ground robots, sufficient motion excitation is often unavailable, resulting in slow convergence or even divergence of iterative algorithms. To address the issues of heavy dependence on motion excitation, slow convergence, and high computational load during the visual odometry initialization phase of ORB-SLAM3, we propose an improved IMU initialization algorithm based on analytical solutions. Within the ORB-SLAM3 framework, the proposed algorithm replaces the original iterative optimization process with a non-iterative strategy. By constructing polynomial equations for gravity, scale, and accelerometer bias, the optimal estimates of system parameters are directly obtained through root solving. This eliminates the sensitivity of iterative algorithms to initial guesses and the risk of divergence. It mainly comprises two core steps: (1) using rotation constraints to independently estimate and scale-decouple gyroscope bias and (2) constructing a constrained linear system to analytically solve for the remaining inertial parameters. In addition, this method only bears a small part of the computational burden of the original algorithm, saving computing resources and substantially improving the IMU initialization speed. Simultaneously, owing to the high robustness of the proposed algorithm without the need for good initial values, it optimizes the motion excitation threshold, effectively solving the problem of insufficient observability under weak motion conditions. Experimental results on the EuRoC dataset show that the proposed algorithm maintains comparable positioning accuracy to the original ORB-SLAM3 algorithm and outperforms the VINS Fusion algorithm while considerably reducing the requirement for motion excitation. Particularly in low-speed and steady-motion scenarios, the IMU initialization time is reduced by approximately 40%, effectively solving the problems of long initialization time and divergence while substantially improving the system’s real-time performance and robustness. The results demonstrate that the proposed algorithm has high precision, high efficiency, and strong robustness, considerably enhancing the engineering practicality of visual inertial systems in weak motion excitation environments.