MCVO: A Generic Visual Odometry for Arbitrarily Arranged Multi-Cameras (2412.03146v2)

Abstract: Making multi-camera visual SLAM systems easier to set up and more robust to the environment is attractive for vision robots. Existing monocular and binocular vision SLAM systems have narrow sensing Field-of-View (FoV), resulting in degenerated accuracy and limited robustness in textureless environments. Thus multi-camera SLAM systems are gaining attention because they can provide redundancy with much wider FoV. However, the usual arbitrary placement and orientation of multiple cameras make the pose scale estimation and system updating challenging. To address these problems, we propose a robust visual odometry system for rigidly-bundled arbitrarily-arranged multi-cameras, namely MCVO, which can achieve metric-scale state estimation with high flexibility in the cameras' arrangement. Specifically, we first design a learning-based feature tracking framework to shift the pressure of CPU processing of multiple video streams to GPU. Then we initialize the odometry system with the metric-scale poses under the rigid constraints between moving cameras. Finally, we fuse the features of the multi-cameras in the back-end to achieve robust pose estimation and online scale optimization. Additionally, multi-camera features help improve the loop detection for pose graph optimization. Experiments on KITTI-360 and MultiCamData datasets validate its robustness over arbitrarily arranged cameras. Compared with other stereo and multi-camera visual SLAM systems, our method obtains higher pose accuracy with better generalization ability. Our codes and online demos are available at https://github.com/JunhaoWang615/MCVO

• The paper introduces MCVO, a novel framework that fuses data from arbitrarily arranged cameras to expand field of view and improve pose estimation.
• It integrates SuperPoint-based feature extraction with LK optical flow and GPU acceleration to reduce CPU load and enhance tracking robustness.
• Evaluation on KITTI-360 and MultiCamData shows improved performance over ORB-SLAM3, notably reducing absolute trajectory error in challenging scenarios.

Review of "MCVO: A Generic Visual Odometry for Arbitrarily Arranged Multi-Cameras"

This paper presents MCVO, an innovative approach to visual odometry that accommodates the flexibility of arbitrarily arranged multi-camera systems. The work by Yu et al. is a substantive contribution to the field of robotic vision, addressing the constraints prevalent in current SLAM systems associated with camera configuration and processing efficiency.

Overview of the Approach

MCVO leverages a multi-camera setup to enhance the robustness of visual odometry (VO). The primary advantage stems from the increased field of view (FoV) and greater resilience to textureless environments. The authors propose a framework that eschews the need for overlapping fields of view or integration with IMUs commonly used in existing methods.

The authors introduce a pipeline that encompasses several stages: feature extraction, pose and metric scale initialization, backend optimization, and loop closure. A notable innovation is the integration of a learning-based feature extraction and tracking front-end, which notably reduces CPU load through GPU acceleration. This component is crucial given the computational demands of processing multiple synchronized camera streams.

Technical Highlights

• Feature Extraction and Tracking: The paper argues for employing SuperPoint-based extraction combined with LK optical flow tracking. This choice is substantiated by experimental evidence showing enhanced feature robustness and reduced CPU usage due to GPU acceleration.
• Scale and Pose Initialization: The work introduces a resilient scale estimation approach based on trajectory consistency across multiple cameras. Unlike traditional methods that rely on camera overlap or additional sensory inputs like IMUs, the MCVO method uses purely visual data to establish metric scale.
• Pose Graph and Loop Closure: The system benefits from a sophisticated multi-camera loop closure mechanism, bolstered by Bag of Words (BoW) models encompassing features from all cameras. This method increases the reliability and frequency of loop detection, an area where multi-camera systems typically exhibit pronounced advantages due to extended FoV.

Evaluation and Results

The authors validate MCVO on the KITTI-360 and MultiCamData datasets, demonstrating superior pose estimation accuracy and robustness to alternative systems such as ORB-SLAM3 and MultiCamSLAM. Notably, it maintains consistent accuracy despite non-overlapping camera configurations, underscoring the flexibility and generalizability of the approach. Quantitative metrics, including absolute trajectory error (ATE), indicate substantial improvements over existing benchmarks, especially in challenging scenarios characterized by large inter-frame displacements.

Implications

Practically, MCVO offers significant improvements in SLAM applications for autonomous systems, particularly in environments where rigid camera configurations and IMU integration are not feasible. The insights into efficient GPU usage for feature extraction could inspire similar approaches in computationally constrained scenarios.

Theoretically, the methodology introduces new avenues for research into VO systems that prioritize camera arrangement flexibility over conventional configurations. It challenges the assumption of overlap necessity in stereo vision and proposes a robust alternative through multi-camera data fusion and trajectory consistency.

Speculation on Future Developments

Future work might extend MCVO by incorporating semantic understanding or learning-based depth inference to further refine system robustness and accuracy. Given the growing synergy between computer vision and machine learning, there is potential for MCVO to evolve with real-time learning mechanisms to enhance feature robustness dynamically.

Continued research could explore the deployment of MCVO in broader contexts, such as underwater or aerial robotics, where arbitrary camera configurations and robustness to various environmental textures are crucial. Additionally, development of specialized hardware capable of handling extensive feature processing on sensor chips themselves could transform the deployment landscape of such flexible multi-camera systems.

In conclusion, this paper represents a pivotal step towards more flexible, efficient, and robust visual odometry systems, with significant implications for future research and practical applications in robotics and autonomous systems.