5 accepted papers
Dense SLAM with a monocular camera remains a highly challenging task. In this paper, we present AMG-SLAM, a novel dense monocular SLAM system that tightly couples sparse tracking with dense Gaussian mapping to achieve fully online and high-quality surface reconstruction. In the frontend, learning-ba…
Although 3D Gaussian Splatting (3DGS) has achieved impressive performance in real-time rendering, its unordered Gaussians make level-of-detail (LoD) construction and model compression highly challenging, limiting its applicability in customized scenarios.In this work, we propose a learning-based Gau
Terrain classification is essential for traversability estimation and planning of unmanned ground vehicles (UGVs) in complex environments. Most existing approaches utilize fully supervised learning to classify terrains based on either exteroceptive or proprioceptive sensor modalities. However, visio
Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper we propose Adaptive VIO a new monocular visual-inert…
In recent years, Visual-Inertial Odometry (VIO) has demonstrated remarkable results using low-cost and complementary sensors. However, these methods often encounter initialization failure and suffer reduced robustness or low trajectory accuracy under challenging scenarios. In this letter, we propose