VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough (2104.06800v1)

Abstract: We present a dense-indirect SLAM system using external dense optical flows as input. We extend the recent probabilistic visual odometry model VOLDOR [Min et al. CVPR'20], by incorporating the use of geometric priors to 1) robustly bootstrap estimation from monocular capture, while 2) seamlessly supporting stereo and/or RGB-D input imagery. Our customized back-end tightly couples our intermediate geometric estimates with an adaptive priority scheme managing the connectivity of an incremental pose graph. We leverage recent advances in dense optical flow methods to achieve accurate and robust camera pose estimates, while constructing fine-grain globally-consistent dense environmental maps. Our open source implementation [https://github.com/htkseason/VOLDOR] operates online at around 15 FPS on a single GTX1080Ti GPU.

• The paper introduces a dense-indirect SLAM framework that extends VOLDOR using geometric priors and dense optical flow to enhance robustness and mapping accuracy.
• It integrates a custom back-end with adaptive priority strategies to achieve scalable and globally consistent pose graph optimization.
• Experimental results on the TartanAir dataset show superior real-time performance (~15 FPS) and improved accuracy over traditional methods.

VOLDOR$^+$SLAM: An Enhanced SLAM System Leveraging Dense Optical Flow

The paper presented introduces VOLDOR$^+$SLAM, a Simultaneous Localization and Mapping (SLAM) framework that employs an innovative dense-indirect method, enhancing the existing VOLDOR visual odometry system. This paper addresses limitations in traditional feature-based and direct methods by leveraging dense optical flows and geometric priors to enhance robustness and accuracy in SLAM applications.

Key Contributions

1. Dense-Indirect Approach: VOLDOR$^+$SLAM extends the VOLDOR probabilistic visual odometry (VO) model to include geometric priors. This enables reliable bootstrapping from monocular data while supporting stereo and RGB-D image sources. The dense-indirect method effectively utilizes advances in optical flow to achieve robust camera pose estimates and dense environmental maps.
2. Custom Back-End: The proposed system tightly integrates geometric estimates with an adaptive priority strategy, which manages the connectivity of an incremental pose graph. This integration ensures global consistency and scalability in large-scale environments.
3. High Performance: The authors demonstrate that VOLDOR$^+$SLAM operates efficiently, achieving approximately 15 FPS on a single GTX1080Ti GPU. This capability is crucial for applications requiring real-time processing, such as robotics and autonomous driving.
4. Open Source Implementation: The research provides an open-source version, facilitating further experimentation and adoption within the research community. This move encourages transparency and collaborative development to enhance SLAM methods further.

Analysis of Methods

VOLDOR$^+$SLAM advances the dense-indirect VO approach by incorporating depth map priors into its probabilistic framework. This addition allows the system to use stereo or RGB-D inputs effectively, improving estimation accuracy across various sensors. The VO front-end operates over batches of optical flows, estimating scene structure and camera poses, while adaptively determining keyframe selections.

The back-end approach manages spatial relationships through keyframe alignments and loop closure detection. The Bayesian framework's innovations ensure robust point-to-plane alignments and seamless integration of photometric consistency for depth images. By prioritizing connectivity based on co-visibility, VOLDOR$^+$SLAM efficiently establishes pose graph constraints to maintain consistent mapping.

Evaluation and Results

The authors conducted extensive evaluations on the TartanAir dataset, which simulates challenging real-world conditions with dynamic objects and varying lighting scenarios. VOLDOR$^+$SLAM showed superior robustness and accuracy in pose estimation compared to ORB-SLAM3 and DSO under both stereo and monocular conditions. Depth map quality is particularly noteworthy, demonstrating high inlier rates and low EPE metrics, outperforming benchmarks such as GA-Net.

Implications and Future Directions

This research highlights the potential of dense-indirect methods in handling the intricacies of SLAM tasks, offering a resilient framework suitable for diverse environments. Practically, the system could significantly benefit applications in autonomous navigation and augmented reality by enhancing environmental mapping accuracy and computational efficiency.

Theoretically, the integration of geometric priors within a dense-indirect SLAM method opens pathways for future research. Potential developments could involve tighter integration with learning-based depth and pose estimation models or exploring novel registration processes. Continued research in improving scalability and handling dynamic changes will be pivotal in advancing such systems to fully autonomous platforms.

VOLDOR$^+$SLAM's novel contributions and experimental results establish it as a significant development in SLAM research, paving the way for future exploration and enhancement of dense-indirect methodologies.