SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
This work addresses limitations in RGB-D odometry for robotics and computer vision applications, representing an incremental improvement by integrating depth estimates with sensor data.
The paper tackled the problem of incomplete and noisy depth maps from active depth cameras affecting RGB-D odometry performance by introducing a visual odometry method that combines depth sensor measurements with probabilistic depth estimates from camera motion for point and line features. Results on large indoor and outdoor scenes demonstrated that this approach overcomes the absence and inaccuracy of depth measurements.
Active depth cameras suffer from several limitations, which cause incomplete and noisy depth maps, and may consequently affect the performance of RGB-D Odometry. To address this issue, this paper presents a visual odometry method based on point and line features that leverages both measurements from a depth sensor and depth estimates from camera motion. Depth estimates are generated continuously by a probabilistic depth estimation framework for both types of features to compensate for the lack of depth measurements and inaccurate feature depth associations. The framework models explicitly the uncertainty of triangulating depth from both point and line observations to validate and obtain precise estimates. Furthermore, depth measurements are exploited by propagating them through a depth map registration module and using a frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D reprojection errors, independently. Results on RGB-D sequences captured on large indoor and outdoor scenes, where depth sensor limitations are critical, show that the combination of depth measurements and estimates through our approach is able to overcome the absence and inaccuracy of depth measurements.