By integrating self-localization, environment mapping, and dynamic object tracking into a unified framework, visual simultaneous localization and mapping with multiple object tracking (SLAMMOT) enhances decision-making and interaction capabilities in applications such as autonomous driving, robotic navigation, and augmented reality. While numerous outstanding visual SLAMMOT methods have been proposed, the majority rely only on point features, overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints. To address this limitation, we propose OP (object planar) -SLAM, an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy. Specifically, we introduce an accurate object planar feature extraction and association method using normal images, alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization. The proposed system is evaluated on both synthetic and public real-world datasets, including Oxford multimotion dataset (OMD) and KITTI tracking dataset. Especially on the OMD, where planar features are prominent, our method improves object pose estimation accuracy by approximately 60%. Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction, achieving notable performance compared with existing methods. Furthermore, OP-SLAM runs in real time, making it suitable for practical robots and augmented reality applications.
A Fri, study studied this question.