Efficient Constellation-Based Map-Merging for Semantic SLAM

Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable `duplicate' landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loop-closure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of `best' hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation (ICRA) 201

Collective cluster-based map merging in multi robot SLAM

New challenges arise with multi-robotics, while information integration is among the most important problems need to be solved in this field. For mobile robots, information integration usually refers to map merging . Map merging is the process of combining partial maps constructed by individual robots in order to build a global map of the environment. Different approaches have been made toward solving map merging problem. Our method is based on transformational approach, in which the idea is to find regions of overlap between local maps and fuse them together using a set of transformations and similarity heuristic algorithms. The contribution of this work is an improvement made in the search space of candidate transformations. This was achieved by enforcing pair-wise partial localization technique over the local maps prior to any attempt to transform them. The experimental results show a noticeable improvement (15-20%) made in the overall mapping time using our technique

다중 로봇 SLAM을 위한 상관관계 기반 지도병합 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이범희.Multi-robot simultaneous localization and mapping (SLAM) is an advanced technique used by multiple robots and autonomous vehicles to build up a collective map within an unknown environment, or to update a collective map within a known environment, while at the same time keeping track of their current location. The collective map is obtained by merging individual maps built by different multiple robots exploring the environment. When robots do not know their initial poses one another, the problem of map merging becomes challenging because the robots have different coordinate systems. If robot-to-robot measurements are not available, the problem of map merging becomes more challenging because the map transformation matrix (MTM) among robots cannot be computed directly. This dissertation presents novel map merging techniques based on the analysis of the correlation among the individual maps, which do not need the knowledge of the relative initial poses of robots and the robot-to-robot measurements. After the cross-correlation function among the spectrometric or tomographic information extracted from the individual maps is generated, the MTM is computed by taking the rotation angle and the translation amounts corresponding to the maximum cross-correlation values. The correlation-based map merging techniques with spectral information presented in this dissertation are the extensions of a conventional map merging technique. One extension is spectrum-based feature map merging (SFMM), which extracts the spectral information of feature maps from virtual supporting lines and computes the MTM by matching the extracted spectral information. The other extension is enhanced-spectrum-based map merging (ESMM), which enhances grid maps using the locations of visual objects and computes the MTM by matching the spectral information extracted from the enhanced grid maps. The two extensions overcome successfully the limitation of the conventional map merging technique. The correlation-based map merging technique with tomographic information is a new map merging technique, which is named tomographic map merging (TMM). Since tomographic analysis can provide more detailed information on grid maps according to rotation and translation than spectral analysis, the more accurate MTM can be computed by matching the tomographic information. The TMM was tested on various pairs of partial maps from real experiments in indoor and outdoor environments. The improved accuracy was verified by showing smaller map merging errors than the conventional map merging technique and several existing map merging techniques.Chapter 1 Introduction 1.1 Background and motivation 1.2 Related works 1.3 Contributions 1.4 Organization Chapter 2 Multi-Robot SLAM and Map Merging 2.1 SLAM using Particle Filters 2.2 Multi-Robot SLAM (MR-SLAM) 2.2.1 MR-SLAM with Known Initial Correspondences 2.2.2 MR-SLAM with Unknown Initial Correspondences 2.3 Map Merging Chapter 3 Map Merging based on Spectral Correlation 3.1 Spectrum-based Map Merging (SMM) 3.2 Spectrum-based Feature Map Merging (SFMM) 3.2.1 Overview of the SFMM 3.2.2 Problem Formulation for the SFMM 3.2.3 Virtual Supporting Lines (VSLs) 3.2.4 Estimation of Map Rotation with Hough Spectra 3.2.5 Rasterization of Updated Feature Maps with VSLs 3.2.6 Estimation of Map Displacements 3.3 Enhanced-Spectrum-based Map Merging (ESMM) 3.3.1 Overview of the ESMM 3.3.2 Problem Formulation for the ESMM 3.3.3 Preprocessing – Map Thinning 3.3.4 Map Enhancement 3.3.5 Estimation of Map Rotation 3.3.6 Estimation of Map Translations Chapter 4 Map Merging based on Tomographic Correlation 4.1 Overview of the TMM 4.2 Problem Formulation for the TMM 4.3 Extraction of Sinograms by the Radon Transform 4.4 Estimation of a Rotation Angle 4.5 Estimation of X-Y Translations Chapter 5 Experiments 5.1 Experimental Results of the SFMM 5.2 Experimental Results of the ESMM 5.2.1 Results in a Parking Area 5.2.2 Results in a Building Roof 5.3 Experimental Results of the TMM 5.3.1 Results in Indoor Environments 5.3.2 Results in Outdoor Environments 5.3.3 Results with a Public Dataset 5.3.4 Results of Merging More Maps 5.4 Comparison among the Proposed Techniques 5.5 Discussion Chapter 6 Conclusions BibliographyDocto

Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO