Abstract
Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger drift errors during long-distance unidirectional tracking. In this paper, we propose a novel geometric integration method that makes use of both 2D and 3D correspondences for RGB-D tracking. Our method handles the problem by exploring visual features both when depth information is available and when it is unknown. The system comprises two parts: coarse pose tracking with 3D correspondences, and geometric integration with hybrid correspondences. First, the coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model, along with 3D correspondences, 2D-3D correspondences and 2D correspondences identified from frame pairs. The initial 3D location of the correspondence is determined in two ways, from depth image and by using the initial poses to triangulate. The model improves the camera poses and decreases drift error during long-distance RGB-D tracking iteratively. Experiments were conducted using data sequences collected by commercial Structure Sensors. The results verify that the geometric integration of hybrid correspondences effectively decreases the drift error and improves mapping accuracy. Furthermore, the model enables a comparative and synergistic use of datasets, including both 2D and 3D features.
Highlights
Creating detailed 3D maps of indoor environments is critical for mobile robotics applications, including indoor navigation, localization and path planning
We propose a geometric integration model to improve the mapping accuracy of the RGB-D systems by globally minimizing the re-projection error of 2D and 3D
We evaluated mapping accuracy with four sets of RGB-D sequences recorded in a tunnel fr2_rpy
Summary
Creating detailed 3D maps of indoor environments is critical for mobile robotics applications, including indoor navigation, localization and path planning. Simultaneous localization and mapping (SLAM) is key to reliable 3D maps, as it estimates the camera pose accurately, regardless of sensors [1]. The widespread availability of RGB-D sensors (such as Kinect and Structure Sensor devices) has led to rapid progress in indoor mapping. They are low cost, lightweight and highly flexible and are capable of high-quality 3D perception [2,3]. Despite these advantages, RGB-D’s depth measurements are limited by range (e.g., typically 0.5 to 3.5 m) [4,5]
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