MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System (1610.07336v1)

Abstract: The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w.r.t the observed environment (scene). In recent years many vision based systems that perform simultaneous localization and mapping (SLAM) have been presented and released as open source. In this paper, we extend and improve upon a state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model. In addition, we include a performance evaluation on accurate ground truth and compare the robustness of the proposed method to a single camera version of the SLAM system. An open source implementation of the proposed multi-fisheye camera SLAM system can be found on-line https://github.com/urbste/MultiCol-SLAM.

• The paper introduces a novel multi-keyframe approach that leverages images from multiple cameras for enhanced mapping.
• It employs a hyper-graph formulation and advanced pose estimation techniques (GP3P, UPnP) to reduce drift and improve loop closure.
• Experimental results show significant improvements in accuracy, reducing both absolute and relative trajectory errors in challenging environments.

MultiCol-SLAM: A Modular Real-Time Multi-Camera SLAM System

The paper "MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System" by S. Urban and S. Hinz presents an innovative approach to enhancing simultaneous localization and mapping (SLAM) systems by leveraging multiple cameras and the MultiCol model. This research addresses the need for robust and continuous estimation of camera position and orientation in various vision-based applications, such as robotics and autonomous vehicles.

Overview and Methodology

MultiCol-SLAM extends existing SLAM frameworks to accommodate arbitrary, rigidly coupled multi-camera systems (MCS) using the MultiCol model. The framework is based on ORB-SLAM and introduces several key modifications, including:

1. Multi-Keyframes (MKFs): A novel concept where each keyframe consists of multiple images from different cameras, enhancing the reconstruction capability in varied environments.
2. Hyper-Graph Formulation: The paper models MultiCol via a hyper-graph, allowing for more complex interactions between various parameters compared to conventional SLAM graphs.
3. Loop Closing and Relocalization: Enhanced methodologies for detecting and correcting loops using a multi-camera setup, improving robustness and drift correction.
4. Pose Estimation Techniques: Incorporating methods like GP3P and UPnP for solving pose estimation challenges from multiple camera observations, improving the accuracy of the SLAM process.

The authors provide an open-source implementation, facilitating further research and development in multi-camera egomotion estimation.

Experimental Results

The results demonstrate significant improvements in accuracy and robustness when using multi-camera configurations compared to single-camera setups. The system shows remarkable advancement in:

• Absolute Trajectory Error (ATE): Reduced error rates in complex environments, indicating enhanced global accuracy.
• Relative Pose Error (RPE): Improved local accuracy and reduced drift over the trajectory.

The experimental comparisons also highlight the limitations of single-camera SLAM, particularly in initialization robustness and small baseline scenarios.

Implications

From a theoretical perspective, this research establishes a foundation for integrating multiple cameras in SLAM, potentially inspiring further developments in the field of photogrammetry and computer vision. Practically, the implications extend to improving real-time navigation capabilities in robots and vehicles, especially in dynamic or feature-sparse environments.

Future Directions

Future research could explore:

• Optimizations for Real-Time Performance: Given the increased computational demand of processing multi-camera inputs, targeting enhanced efficiency remains critical.
• Scalability to Other Sensor Systems: Expanding the MultiCol framework to integrate with other types of sensors, such as LiDAR, could provide more comprehensive environmental mapping capabilities.
• Advanced Feature Detection: Investigation into feature detectors specifically suited for fisheye and omnidirectional cameras could further enhance system performance.

Conclusion

MultiCol-SLAM provides a robust, modular approach to multi-camera SLAM that significantly enhances accuracy and reliability. By addressing key challenges and offering a publicly available implementation, this paper contributes valuable insights and tools to the SLAM research community. Such advancements pave the way for more sophisticated autonomous systems capable of navigating complex environments with greater precision.