Direct Monocular Odometry Using Points and Lines (1703.06380v1)

Abstract: Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments and is also more robust to lighting changes and fast motion by increasing the convergence basin. We maintain a depth map for the keyframe then in the tracking part, the camera pose is recovered by minimizing both the photometric error and geometric error to the matched edge in a probabilistic framework. In the mapping part, edge is used to speed up and increase stereo matching accuracy. On various public datasets, our algorithm achieves better or comparable performance than state-of-the-art monocular odometry methods. In some challenging texture-less environments, our algorithm reduces the state estimation error over 50%.

• The paper introduces a VO algorithm that fuses point and edge features, significantly improving traditional monocular approaches.
• It employs a combined photometric and geometric error minimization strategy to enhance pose recovery in challenging lighting and texture conditions.
• Experimental results show over a 50% reduction in estimation error, reinforcing its potential for robust autonomous navigation and mapping.

Direct Monocular Odometry Using Points and Lines

The paper presented by Shichao Yang and Sebastian Scherer introduces a novel approach to visual odometry (VO) using monocular cameras, emphasizing the integration of points and edges. This research is situated in the broader context of VO and SLAM technologies which play significant roles in robot navigation, 3D reconstruction, and virtual reality. The researchers propose an algorithm that efficiently combines point features and edge features, addressing some inherent limitations associated with previous methods that relied heavily on either point-based or direct pixel intensity techniques.

Technical Overview

Traditionally, monocular VO methods have predominantly focused on feature points through extraction and matching or by direct methods that minimize pixel-to-pixel photometric errors. However, Yang and Scherer's approach shifts this focus towards incorporating edges alongside points. Edges, as geometric entities, are robust against lighting changes and can be detected even in environments with limited texture. This robustness is crucial for enhancing VO in challenging scenarios defined by rapid motion or variable lighting conditions.

The algorithm is structured around maintaining a depth map for keyframes, then employing both photometric and geometric errors to recover camera poses in the tracking phase. In mapping, edges serve to expedite stereo matching processes, enhancing accuracy. On various publicly available datasets, this method demonstrates superior performance to existing state-of-the-art monocular odometry algorithms, especially in environments lacking texture, with a reduction in state estimation error over 50%.

Strong Results and Analytical Insights

The paper highlights its main contributions in providing:

• A real-time monocular VO algorithm that efficiently incorporates points and edges, rendering it particularly effective in texture-less environments.
• An uncertainty analysis and probabilistic fusion of observation models, which includes both points and lines for tracking and mapping.
• The development of analytical edge-based regularization techniques.

Numerical results from datasets such as TUM RGBD and ICL-NUIM showcase the algorithm's ability to outperform or perform comparably to other state-of-the-art monocular odometry methods, notably in scenarios with sparse point features.

Implications and Future Directions

The inclusion of edges in VO not only enhances the robustness of pose estimation but also enriches environmental mapping. It addresses some critical issues in monocular applications where depth perception is inherently limited. Practically, these improvements can lead to more reliable navigation systems in autonomous vehicles and drones, especially within environments scarce in distinguishing features or subjected to variable lighting conditions. Theoretically, these insights pave a pathway towards optimizing computational strategies in VO algorithms, potentially influencing developments in mixed sensing methods that utilize RGB-D data or stereo setups.

Looking forward, the paper suggests avenues for reducing computational overhead associated with edge detection and mapping. Direct edge alignment and the incorporation of bundle adjustment across multiple frames using both points and edges could further enhance algorithm performance. Moreover, the integration of additional geometrical primitives like planes, as indicated by the possibility of employing POPSLAM frameworks, represents another promising direction.

In summary, Yang and Scherer's work contributes significantly to the field of visual odometry by extending the operational scope of monocular systems through the strategic incorporation of edges, thus setting the stage for more advanced and robust applications within robotic and AI systems.