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SegMatch: Segment based loop-closure for 3D point clouds (1609.07720v2)

Published 25 Sep 2016 in cs.RO

Abstract: Loop-closure detection on 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of "perfect segmentation", or on the existence of "objects" in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm will be made available after publication.

Citations (299)

Summary

  • The paper introduces SegMatch, a novel approach for 3D point cloud place recognition that uses segment-based features to achieve robustness against environmental changes, unlike traditional keypoint methods.
  • Evaluated on the KITTI dataset, SegMatch demonstrates superior precision, computational efficiency (1Hz), and robust loop-closure detection with minimized false positives compared to keypoint-based systems.
  • SegMatch's segment-level processing improves localization and mapping in complex environments, holding promise for enhancing SLAM operations and integrating future learning-based techniques.

Overview of "SegMatch: Segment Based Place Recognition in 3D Point Clouds"

The paper introduces SegMatch, a novel approach to place recognition in 3D point clouds, particularly useful in the domain of robotics. Traditional place recognition methods often adapt techniques from image-based solutions. However, these methods tend to struggle with robustness under environmental changes and viewpoint dependencies. SegMatch addresses these limitations by shifting the focus to segment-based approaches, leveraging the strengths of local and global feature methodologies without relying on assumptions of perfect segmentation or object presence.

Key Contributions and Methodology

SegMatch fundamentally operates by processing inputs from 3D laser range finders, extracting segments from these point clouds, and tracking these segments to recognize places. Its architecture consists of a modular design that includes four primary stages: point cloud segmentation, feature extraction, segment matching, and geometric verification. The segmentation process involves dividing point clouds into clusters based on inherent characteristics without depending on predefined object definitions, allowing it to cope with unstructured environments.

A highlight of SegMatch is its use of various descriptors for feature extraction, focusing notably on eigenvalue-based and ensemble of shape histograms. Through a machine learning approach, specifically employing a random forest classifier, SegMatch effectively identifies correspondence between segment features in different environments. This step facilitates both high accuracy and real-time applicability.

Evaluation and Results

For its evaluation, SegMatch was tested against the KITTI odometry dataset, which provided a robust environment to measure its efficacy in real-world scenarios. Its performance has proven superior to conventional keypoint-based systems in both precision and computational efficiency. Not only did it achieve 1Hz localization frequency, but it also demonstrated robust loop-closure detection capability. False positives were minimized significantly, enhancing the reliability of the segmented-based technique over a keypoint-based approach, which was more prone to errors.

Implications and Future Directions

The implications of SegMatch extend to a wide range of robotic applications, particularly in navigation and mapping tasks where environmental dynamics and scene complexity are significant. By employing segment-based features, the localization and mapping systems in robots can work more efficiently even in scenarios with substantial geometrical complexity or lack of distinct object-based features.

Practically, SegMatch reshapes how place recognition can be approached by using descriptive segments that do not depend on detailed models of object interactions. The real-time loop-closure detection facilitated by SegMatch holds promise in improving SLAM operations significantly, reducing drift over time via effective pose-graph optimization.

Looking forward, further development can be anticipated in extending the segmentation approach with learning-based descriptors that adapt better to diverse environments. The adaptive capabilities of SegMatch's modular nature suggest it could potentially integrate emerging techniques in 3D segmentation and classification, broadening its utility across various sensor modalities and application domains.

In conclusion, SegMatch represents a significant step forward in the domain of 3D point cloud processing, showcasing how segment-level processing can offer a balanced and robust alternative to traditional place recognition methods. Its application in environments where conventional assumptions do not hold makes it a pivotal development in both academic research and real-world robotics scenarios.

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