Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation

Global registration of heterogeneous ground and aerial mapping data is a challenging task. This is especially difficult in disaster response scenarios when we have no prior information on the environment and cannot assume the regular order of man-made environments or meaningful semantic cues. In this work we extensively evaluate different approaches to globally register UGV generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud maps from vision sensors. The approaches are realizations of different selections for: a) local features: key-points or segments; b) descriptors: FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR. Additionally, we compare the results against standard approaches like applying ICP after a good prior transformation has been given. The evaluation criteria include the distance which a UGV needs to travel to successfully localize, the registration error, and the computational cost. In this context, we report our findings on effectively performing the task on two new Search and Rescue datasets. Our results have the potential to help the community take informed decisions when registering point-cloud maps from ground robots to those from aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on Safety, Security, and Rescue Robotics 2017 (SSRR 2017

Dynamic graph-based search in unknown environments

A novel graph-based approach to search in unknown environments is presented. A virtual geometric structure is imposed on the environment represented in computer memory by a graph. Algorithms use this representation to coordinate a team of robots (or entities). Local discovery of environmental features cause dynamic expansion of the graph resulting in global exploration of the unknown environment. The algorithm is shown to have O(k.nH) time complexity, where nH is the number of vertices of the discovered environment and 1 <= k <= nH. A maximum bound on the length of the resulting walk is given

A Real-Time Robust SLAM for Large-Scale Outdoor Environments

International audienceThe problem of simultaneous localization and mapping (SLAM) is still a challenging issue in large-scale unstructured dynamic environments. In this paper, we introduce a real-time reliable SLAM solution with the capability of closing the loop using exclusive laser data. In our algorithm, a universal motion model is presented for initial pose estimation. To further refine robot pose, we propose a novel progressive refining strategy using a pyramid grid-map based on Maximum Likelihood mapping framework. We demonstrate the success of our algorithm in experimental result by building a consistent map along a 1.2 km loop trajectory (an area about 100,000 m2) in an increasingly unstructured outdoor environment, with people and other clutter in real time