VID-SLAM: A New Visual Inertial SLAM Algorithm Coupling an RGB-D Camera and IMU Based on Adaptive Point and Line Features

Abstract

The visual-inertial simultaneous localization and mapping (VI-SLAM), which integrates data from monocular or stereo cameras, has garnered significant attention and development. The RGB-D camera, capable of capturing both color and depth images simultaneously, can perceive a comprehensive view of the surroundings. To fully leverage two types of measurement information from the RGB-D camera and inertial measurement unit (IMU) sensor for accurate pose estimation, we propose a new VI-SLAM algorithm, VID-SLAM, that effectively couples the RGB-D camera with the IMU. In our proposal, we first develop an adaptive point feature detection approach that rapidly detects and tracks sufficient point features. This approach uses adaptive nonmaximum suppression and the KD-Tree algorithm to ensure a homogeneous distribution of point features. Second, we incorporate line features into the pose estimation module of the simultaneous localization and mapping (SLAM) algorithm. By screening line features based on the geometric properties of vanishing points, we ensure that the detected lines align with the edges of scene objects as early as possible. Beyond the 2-D reprojection error of line features, we introduce a new error term that leverages the geometric constraints of plane normal vectors formed by matched line features and the optical center of the RGB-D camera; furthermore, we estimate the pose of the RGB-D camera by loosely coupling point-line visual features with IMU preintegration measurements. In the back end of VID-SLAM, we tightly couple the point-line feature error model with the IMU preintegration to jointly optimize the camera pose. Extensive qualitative and quantitative comparisons demonstrate that our VID-SLAM algorithm achieves robust performance and comparable accuracy.