DDIO-Mapping: A Fast and Robust Visual-Inertial Odometry for Low-Texture Environment Challenge
Xinyu Jiang, Heng Li, Chuangquan Chen, Yongquan Chen, Junlang Huang, Zuguang Zhou, Yimin Zhou, Chi‐Man Vong
Abstract
Accurate localization and pose estimation remain challenging for autonomous robots in low-texture environment. This article proposes a tightly coupled direct depth-inertial odometry and mapping (DDIO-Mapping) framework to simultaneously tackle three crucial issues in such environments: 1) ineffective feature point extraction; 2) inefficient searching of feature points; and 3) imbalanced feature extraction under uneven illumination conditions. In DDIO-Mapping, a novel robust strategy is designed that combines grayscale and depth features for optimization instead of only the RBG features in the existing methods. To improve searching efficiency, a new RGBD feature extraction is applied to directly extract both the depth and grayscale features from the RGBD images, which only requires searching the feature points in the 2-D space rather than the enormous 3-D space in K-dimensional (KD) tree. To deal with imbalanced feature extraction, a feature filtering and selection strategy is proposed to adaptively adjust the depth and grayscale weightage. Finally, with the effectively extracted features from RGBD images, a new nonlinear tightly coupled inverse depth residual function is customized to accurately estimate the optimal pose in low-texture environments. The framework is highly robust, accurate, and efficient. Experiments demonstrate that DDIO-Mapping reduces the root-mean-square error by approximately 30% compared to other state-of-the-art algorithms while retaining the same efficiency of approximately 20–35 ms.