When robots perform localization in indoor low-light environments, factors such as weak and uneven lighting can degrade image quality. This degradation results in a reduced number of feature extractions by the visual odometry front end and may even cause tracking loss, thereby impacting the algorithm’s positioning accuracy. To enhance the localization accuracy of mobile robots in indoor low-light environments, this paper proposes a visual inertial odometry method (L-MSCKF) based on the multi-state constraint Kalman filter. Addressing the challenges of low-light conditions, we integrated Inertial Measurement Unit (IMU) data with stereo vision odometry. The algorithm includes an image enhancement module and a gyroscope zero-bias correction mechanism to facilitate feature matching in stereo vision odometry. We conducted tests on the EuRoC dataset and compared our method with other similar algorithms, thereby validating the effectiveness and accuracy of L-MSCKF.

Ultra-Wideband (UWB) technology is widely used in indoor positioning due to its high accuracy and strong resistance to interference. However, practical UWB positioning systems still encounter challenges such as random errors and asymmetric base station topology, which significantly affect positioning accuracy. To address these issues, this paper proposes an Adaptive Vector Projection Iteration algorithm based on the Least Squares method (LS-AVPI). Firstly, the Sparrow Search Algorithm (SSA) is employed to identify optimal parameters, with the fitness function based on an error suppression model established through vector projection angles and iterative concepts. The optimal parameters obtained from the SSA are then used to train a Convolutional Neural Network (CNN) for accurate prediction of unknown data. Finally, the predicted parameters are input into the error suppression model to solve for the final position. Experimental results demonstrate that the LS-AVPI algorithm significantly outperforms the constrained weighted, the iterative, the standard, and the two-step weighted least squares methods, with accuracy improvements of 57.3%, 62.7%, 65.7%, and 54.8%, respectively. This study not only effectively improves UWB positioning accuracy but also provides a new technical approach for achieving high-precision positioning in complex environments, offering significant theoretical and practical value.