PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments (1705.09479v2)

Abstract: Traditional approaches to stereo visual SLAM rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in 3D elements, which can be exploited to infer valuable, high-level scene structures like planes, empty spaces, ground plane, etc. (not addressed in this work). Our proposal has been tested with several popular datasets (such as KITTI and EuRoC), and is compared to state of the art methods like ORB-SLAM, revealing a more robust performance in most of the experiments, while still running in real-time. An open source version of the PL-SLAM C++ code will be released for the benefit of the community.

• The paper introduces a novel stereo SLAM method that integrates point and line segment features to enhance scene understanding and localization.
• It leverages combined ORB and LSD-LBD feature tracking paired with optimized bundle adjustment to achieve robust mapping in low-texture conditions.
• Experimental results on KITTI and EuRoC MAV datasets show improved accuracy and reduced drift compared to traditional point-only SLAM approaches.

Overview of PL-SLAM: A Stereo SLAM System Utilizing Points and Line Segments

The paper "PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments" presents a novel approach in the domain of stereo visual Simultaneous Localization and Mapping (SLAM). This system is engineered to enhance robustness across diverse environments, particularly those with low texture where conventional point-feature-based methods face challenges. It accomplishes this by integrating point and line segment features throughout its SLAM pipeline, including visual odometry, keyframe selection, and bundle adjustment, while also adopting a new loop closure detection strategy.

Key Contributions and Methodology

PL-SLAM innovates by amalgamating point and line segment features, enabling better scene understanding and camera localization in texturally deficient environments. By utilizing both geometrical and feature-based representations, the system constructs richer maps that include more structural information. Key processes within PL-SLAM include:

• Visual Odometry and Feature Tracking: Utilizing a combination of ORB descriptors for points and LSD with LBD for line segments, PL-SLAM achieves accurate feature tracking in both stereo and frame-to-frame contexts. This combination mitigates the shortcomings of point-only or line-only methods in challenging visual conditions.
• Keyframe Insertion and Bundle Adjustment: The system structure ensures efficient local map optimization, seamlessly integrating point and line features into the bundle adjustment process. This enhances the accuracy and stability of the SLAM algorithm without substantial computational overhead.
• Loop Closure and Bag-of-Words Approach: The novel loop closure mechanism involves an extended bag-of-words method that harnesses the descriptive capabilities of both points and lines. This enhances place recognition robustness, crucial for long-term mapping and reducing drift.

Evaluation and Experimental Results

The PL-SLAM system was extensively evaluated using well-known datasets such as KITTI and EuRoC MAV. The results demonstrated a superior consistency and robustness in scenarios where traditional point-only approaches often falter due to lack of features. Notably, in structured indoor environments and low-textured scenes, PL-SLAM exhibited an enhanced performance by providing a balance between the detailed point representation and the structural information offered by line segments.

The results underscore the effectiveness of line segments in maintaining trajectory estimation where keypoint-based methods typically fail. Comparative analysis against ORB-SLAM, a leading benchmark in SLAM systems, revealed that PL-SLAM can outperform point-only methods in terms of maintaining accuracy and trajectory fidelity in varied environmental conditions.

Implications and Future Work

PL-SLAM's integration of dual feature types is a significant advancement in visual SLAM. It offers a practical solution for both academia and industry by promising reliable camera localization and mapping across diverse environments. The system is open-source, encouraging further development and application in mobile robotics.

Future avenues for research include optimization of the computational expenses associated with dual feature processing and expansion of the system into additional sensory modalities or environmental conditions. Furthermore, exploring the application of these enhanced mapping capabilities in semantic mapping or augmented reality scenarios could yield additional utilities.

The contributions of PL-SLAM imply a continued exploration into hybrid feature methodologies, driving forward developments in autonomous navigation systems requiring robust performance in complex, real-world environments.