A visual simultaneous localization and mapping approach based on scene segmentation and incremental optimization

Abstract

Existing visual simultaneous localization and mapping (V-SLAM) algorithms are usually sensitive to the situation with sparse landmarks in the environment and large view transformation of camera motion, and they are liable to generate large pose errors that lead to track failures due to the decrease of the matching rate of feature points. Aiming at the above problems, this article proposes an improved V-SLAM method based on scene segmentation and incremental optimization strategy. In the front end, this article proposes a scene segmentation algorithm considering camera motion direction and angle. By segmenting the trajectory and adding camera motion direction to the tracking thread, an effective prediction model of camera motion in the scene with sparse landmarks and large view transformation is realized. In the back end, this article proposes an incremental optimization method combining segmentation information and an optimization method for tracking prediction model. By incrementally adding the state parameters and reusing the computed results, high-precision results of the camera trajectory and feature points are obtained with satisfactory computing speed. The performance of our algorithm is evaluated by two well-known datasets: TUM RGB-D and NYUDv2 RGB-D. The experimental results demonstrate that our method improves the computational efficiency by 10.2% compared with state-of-the-art V-SLAMs on the desktop platform and by 22.4% on the embedded platform, respectively. Meanwhile, the robustness of our method is better than that of ORB-SLAM2 on the TUM RGB-D dataset.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…It plays an important role for the tracking and localization process, helping in eliminating drift errors for camera poses attached to the robot ( Sheng et al, 2019 ; Hsiao et al, 2017 ). Subsequently, this keyframe is sent for further processing in the next stage, where it will be shaped into a preliminary map, a crucial part for the third stage of the workflow ( Aloui et al, 2022 ; Zhang et al, 2020 ).…”

“…V-SLAM can be applied to mobile robotics that utilizes cameras to create a map of their surroundings and easily locate themselves within their work space ( Li et al, 2020 ). It uses techniques such as computer vision to extract and match visual data for localization and mapping ( Zhang et al, 2020 ; Chung et al, 2023 ). It allows robots to map complex environments while performing tasks such as navigation in dynamic fields ( Placed et al, 2023 ; Khoyani and Amini 2023 ).…”