Swarm SLAM: Challenges and Perspectives

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints

Dynamic Detection of Topological Information from Grid-Based Generalized Voronoi Diagrams

In the context of robotics, the grid-based Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent their surrounding area. Current approaches for incrementally constructing GVDs mainly focus on providing metric skeletons of underlying grids, while the connectivity among GVD vertices and edges remains implicit, which makes high-level spatial reasoning tasks impractical. In this paper, we present an algorithm named Dynamic Topology Detector (DTD) for extracting a GVD with topological information from a grid map. Beyond the construction and reconstruction of a GVD on grids, DTD further extracts connectivity among the GVD edges and vertices. DTD also provides efficient repair mechanism to treat with local changes, making it work well in dynamic environments. Simulation tests in representative scenarios demonstrate that (1) compared with the static algorithms, DTD generally makes an order of magnitude improvement regarding computation times when working in dynamic environments; (2) with negligible extra computation, DTD detects topologies not computed by existing incremental algorithms. We also demonstrate the usefulness of the resulting topological information for high-level path planning tasks

Learning Topometric Semantic Maps from Occupancy Grids

Today's mobile robots are expected to operate in complex environments they share with humans. To allow intuitive human-robot collaboration, robots require a human-like understanding of their surroundings in terms of semantically classified instances. In this paper, we propose a new approach for deriving such instance-based semantic maps purely from occupancy grids. We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map. The extraction is followed by a post-processing chain to further increase the accuracy of our approach, as well as place categorization for the three classes room, door and corridor. All detected and classified entities are described as instances specified in a common coordinate system, while a topological map is derived to capture their spatial links. To train our two neural networks used for detection and map segmentation, we contribute a simulator that automatically creates and annotates the required training data. We further provide insight into which features are learned to detect doorways, and how the simulated training data can be augmented to train networks for the direct application on real-world grid maps. We evaluate our approach on several publicly available real-world data sets. Even though the used networks are solely trained on simulated data, our approach demonstrates high robustness and effectiveness in various real-world indoor environments.Comment: Presented at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions

Indoor Navigation and Manipulation using a Segway RMP

This project dealt with a Segway RMP, utilizing it in an assistive-technology manner, encompassing navigation and manipulation aspects of robotics. First, background research was conducted to develop a blueprint for the robot. The hardware, software, and configuration of the RMP was updated, and a robotic arm was designed to extend the RMP’s capabilities. The robot was programmed to accomplish autonomous multi-floor navigation through the use of the navigation stack in ROS, image detection, and a GUI. The robot can navigate through the hallways of the building utilizing the elevator. The robotic arm was designed to accomplish tasks such as pressing a button and picking an object up off of a table. The Segway RMP is designed to be utilized and expanded upon as a robotics research platform