This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous localization and mapping (SLAM). Existing robust SLAM methods assume a good initialization or an “odometry backbone” to classify inlier and outlier loop closures. In the multi-robot case, these assumptions do not always hold. This paper presents an algorithm called Pairwise Consistency Maximization (PCM) that estimates the largest pairwise internally consistent set of measurements. Finding the largest pairwise internally consistent set can be transformed into an instance of the maximum clique problem from graph theory, and by leveraging the associated literature it can be solved in real time. This paper evaluates how well PCM approximates the combinatorial gold standard using simulated data. It also evaluates the performance of PCM on synthetic and real-world data sets in comparison with DCS, SCGP, and RANSAC, and shows that PCM significantly outperforms these methods.
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Model Quality Aware RANSAC: A Robust Camera Motion Estimator
Robust estimation of camera motion under the presence of outlier noise is a fundamental problem in robotics and computer vision. Despite existing efforts that focus on detecting motion and scene degeneracies, the best existing approach that builds on Random Consensus Sampling (RANSAC) still has non-negligible failure rate. Since a single failure canlead to the failure of the entire visual simultaneous localization and mapping, it is important to further improve the robust estimation algorithm. We propose a new robust camera motion estimator (RCME) by incorporating two main changes: a model-sample consistency test at the model instantiation stepand an inlier set quality test that verifies model-inlier consistency using differential entropy. We have implemented our RCME algorithm and tested it under many public datasets. The results have shown a consistent reduction in failure rate when comparing to the RANSAC-based Gold Standard approach and two recent variations of RANSAC methods.
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- Award ID(s):
- 1925037
- NSF-PAR ID:
- 10206853
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Oct. 25-29, 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Aim Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm
Maxent and the monk parakeet (Myiopsitta monachus ).Location Native range in Southern America and global invasions predominantly in North/Central America and Europe.
Methods We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on area under the receiver operating characteristic curve (
AUC ) and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature.Results Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance.
Main Conclusions Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code and R package, allows such estimates for either the same or a different region/time period, and it merits use and continued development.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
