Multi-robot data mapping simulation by using microsoft robotics developer studio

This document summarizes the goals achieved in the development of a data mapping appli- cation, for a multi-robot system, implemented as a service with the guidelines found in the Service Oriented Computing paradigm (SOC). The obtained service generates both local and global maps in the reconstruction of a virtual scenario: the local maps represent the sur- rounding area around each one of the mobile robots, and the global one the totality of the scenario where the robots move. The information of the global map is continuously updated by merging the data coming from the local maps by using a novel approach: each one of the maps manages a confidence level value that defines which of the data coming from the maps is worthy of being updated into the global one. This technique is not present in related work. The Microsoft Robotics Developer Studio framework was chosen for its implementation because of the advantages that this tool offers in the management of concurrent and distrib- uted processes, typically found in both a robotics platform and in a multi-robot system

Detecting Invalid Map Merges in Lifelong SLAM

For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.Comment: Accepted at IROS 202

MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as robustly merging the sub-maps built by the agents. We take the view that the existing approaches are either aggressive or conservative: Aggressive strategies merge two sub-maps built by different agents together when overlap is detected, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. Conservative strategies direct one agent to revisit an excessive amount of the historical trajectory of another agent for verification before merging, which can lower the exploration efficiency due to the repeated exploration of the same space. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a \emph{cooperative} manner by planning for agents with merged sub-maps together to further improve exploration efficiency. Our experiments show that our approach is up to 50\% more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA

Submap Matching for Stereo-Vision Based Indoor/Outdoor SLAM

Autonomous robots operating in semi- or unstructured environments, e.g. during search and rescue missions, require methods for online on-board creation of maps to support path planning and obstacle avoidance. Perception based on stereo cameras is well suited for mixed indoor/outdoor environments. The creation of full 3D maps in GPS-denied areas however is still a challenging task for current robot systems, in particular due to depth errors resulting from stereo reconstruction. State-of-the-art 6D SLAM approaches employ graph-based optimization on the relative transformations between keyframes or local submaps. To achieve loop closures, correct data association is crucial, in particular for sensor input received at different points in time. In order to approach this challenge, we propose a novel method for submap matching. It is based on robust keypoints, which we derive from local obstacle classification. By describing geometrical 3D features, we achieve invariance to changing viewpoints and varying light conditions. We performed experiments in indoor, outdoor and mixed environments. In all three scenarios we achieved a final 3D position error of less than 0.23% of the full trajectory. In addition, we compared our approach with a 3D RBPF SLAM from previous work, achieving an improvement of at least 27% in mean 2D localization accuracy in different scenarios

AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments

We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile to incorrect data associations, and are primarily limited to working only offline. AutoMerge utilizes multi-perspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, after assembling the segments, AutoMerge performs fine matching and pose-graph optimization to globally smooth the merged map. We demonstrate AutoMerge on both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8). The experiments show that AutoMerge (i) surpasses the second- and third- best methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to temporally-spaced revisits. To the best of our knowledge, AutoMerge is the first mapping approach that can merge hundreds of kilometers of individual segments without the aid of GPS.Comment: 18 pages, 18 figur