Deep Patch Visual SLAM (2408.01654v1)

Abstract: Recent work in visual SLAM has shown the effectiveness of using deep network backbones. Despite excellent accuracy, however, such approaches are often expensive to run or do not generalize well zero-shot. Their runtime can also fluctuate wildly while their frontend and backend fight for access to GPU resources. To address these problems, we introduce Deep Patch Visual (DPV) SLAM, a method for monocular visual SLAM on a single GPU. DPV-SLAM maintains a high minimum framerate and small memory overhead (5-7G) compared to existing deep SLAM systems. On real-world datasets, DPV-SLAM runs at 1x-4x real-time framerates. We achieve comparable accuracy to DROID-SLAM on EuRoC and TartanAir while running 2.5x faster using a fraction of the memory. DPV-SLAM is an extension to the DPVO visual odometry system; its code can be found in the same repository: https://github.com/princeton-vl/DPVO

• The paper introduces DPV-SLAM, extending deep patch visual odometry with robust loop closure for accurate and computationally efficient monocular SLAM.
• It achieves comparable accuracy to systems like DROID-SLAM while running 2.5x faster and using significantly lower memory.
• The approach integrates proximity-based and classical loop closure methods, making it versatile for robotics and augmented reality applications.

Deep Patch Visual SLAM

In this essay, we provide an expert review of the paper titled "Deep Patch Visual SLAM" by Lahav Lipson, Zachary Teed, and Jia Deng. The work discusses advancements in monocular visual SLAM (Simultaneous Localization and Mapping) leveraging deep-learning techniques to improve efficiency, accuracy, and robustness of camera pose estimation in real-world settings.

Overview

Visual SLAM, an extension of the structure-from-motion problem, deals with real-time state estimation from video streams, crucial for applications in robotics and various computer vision tasks. Traditional SLAM systems struggle with accuracy and computational efficiency when dealing with monocular video devoid of inertial measurements. Recent approaches using deep network backbones have achieved notable accuracy but often suffer from substantial resource demands, memory overhead, and fluctuating runtime performance due to contention for GPU resources.

The paper introduces Deep Patch Visual SLAM (DPV-SLAM), a method designed to address these issues by offering a monocular visual SLAM system capable of running on a single GPU with high efficiency. DPV-SLAM extends the Deep Patch Visual Odometry (DPVO) system to incorporate a full SLAM solution with mechanisms for loop closure.

Key Contributions

The primary contributions of DPV-SLAM include:

1. High Efficiency and Low Memory Overhead: DPV-SLAM maintains high minimum framerates (1x-4x real-time), with a relatively low memory overhead (5-7G), substantially better than existing deep SLAM systems.
2. Comparable Accuracy: The system displays comparable accuracy to the DROID-SLAM on datasets such as EuRoC and TartanAir while operating at 2.5x faster speeds.
3. Robust and Generalizable Performance: DPV-SLAM demonstrates strong performance across various environments without requiring retraining, suggesting robust generalization.

Methodology

DPV-SLAM builds upon the DPVO system which employs sparse optical flow to reduce computational overhead while still using deep networks. DPVO, however, lacks mechanisms for correcting accumulated pose errors, which are vital for full SLAM systems. DPV-SLAM introduces a robust loop closure mechanism to address this.

The authors implement two efficient mechanisms to correct drift:

1. Proximity-based Loop Closure: This mechanism detects loop closures using camera proximity, avoiding the significant overhead of storing dense feature maps for every frame. An integrated CUDA-accelerated block-sparse implementation enables efficient bundle adjustment.
2. Classical Loop Closure: This secondary mechanism employs traditional image retrieval and pose graph optimization to correct for scale drift, running on the CPU in parallel to the main process, thereby minimizing runtime overhead.

Experimental Results

Extensive experiments were conducted on several benchmarks: EuRoC, KITTI, TUM-RGBD, and TartanAir. Results indicate:

• On EuRoC: DPV-SLAM achieves an average ATE (Absolute Trajectory Error) of 0.024, closely matching the 0.022 achieved by DROID-SLAM but with significantly lower memory usage and faster runtimes.
• On TUM-RGBD: The system reaches an average ATE of 0.076, demonstrating improved resource efficiency while maintaining accuracy.
• On KITTI: DPV-SLAM++ shows robust performance, handling both indoor and outdoor environments effectively, achieving comparable or superior results to other systems without requiring extensive reconfiguration or retraining.

Implications and Future Directions

DPV-SLAM demonstrates significant advancements in monocular visual SLAM, with strong implications for real-world SLAM applications in robotics and augmented reality. Its efficient resource usage allows for deployment on single-GPU systems, broadening the potential use cases.

Future research could explore:

• Extended Frameworks: Enhancing the current framework to handle more diverse environments and potential integration with sensor fusion techniques.
• Optimization Techniques: Further refinement of the loop closure mechanisms and real-time performance optimizations.
• Enhanced Features: Exploration of additional features like semantic mapping or integration with other deep learning-based perception systems.

Conclusion

The paper makes notable contributions to the SLAM community by addressing critical limitations of existing systems through the introduction of DPV-SLAM. By ensuring high efficiency, low memory overhead, and robust performance across diverse environments, DPV-SLAM stands as a valuable resource for advancing monocular visual SLAM applications. As the community continues to build on these insights, further improvements in both theoretical and practical aspects of SLAM are anticipated.