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

Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very high-dimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.Comment: 7 pages, 4 figures; accepted for presentation in IEEE Int. Conf. on Robotics and Automation, ICRA '20, Paris, France, June 202

Fuel Benefit from Optimal Trajectory Assignment on the North Atlantic Tracks

The North Atlantic Tracks represent one of the highest density international traffic regions in the world. Due to the lack of high-resolution radar coverage over this region, the tracks are subject to more restrictive operational constraints than flights over the continental U.S. Recent initiatives to increase surveillance over the North Atlantic has motivated studies on the total benefit potential for increased surveillance over the tracks. One of the benefits of increased surveillance is increased accessibility of optimal altitude and speed operations over the track system. For a sample of 4033 flights over 12 days from 2014-2015, a fuel burn analysis was performed that calculates the fuel burn from optimal altitude, optimal speed and optimal track trajectories over the North Atlantic Tracks. These results were compared with calculated as-flown fuel burn in order to determine the benefit potential from optimal trajectories. Operation at optimal altitude and speed increased this benefit to 2.83% reduction potential in average fuel burn. Operation at optimal altitude alone, however, reduces the benefit potential to 1.24% reduction in average fuel burn. Optimal track assignment allows for a 3.20% reduction in average fuel burn. For the sample data, 45.1% of flights were unable to access their optimal altitude and speed due to separation requirements. Reduced separation up to 5 nautical miles can decrease the number of conflicts to 14.0%. Reducing the separation requirements both longitudinally and laterally can allow for increased accessibility of optimal altitudes, speeds and track configurations. Pilot decision support tools that increase awareness of aircraft fuel performance by integrating optimal altitude and speed configurations can also reduce aircraft fuel burn. The utility of such a tool is evaluated through a survey on pilot-decision making.This work was funded by the US Federal Aviation Administration (FAA) Office of Environment and Energy as a part of ASCENT Project 15 under Air Force Contract FA8721-05-C-0002. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA or other ASCENT Sponsors

Model-Based Adaptive Behavior Framework for Optimal Acoustic Communication and Sensing by Marine Robots

In this paper, a hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication. The adaptation framework is implemented within the behavior-based mission-oriented operating suite-interval programming (MOOS-IvP) marine autonomy architecture and uses a new embedded high-fidelity acoustic modeling infrastructure, the generic robotic acoustic model (GRAM), to provide real-time estimates of the acoustic environment under changing environmental and situational scenarios. A set of behaviors that combine adaptation to the current acoustic environment with strategies that extend the decision horizon beyond that of typical behavior-based systems have been developed, implemented, and demonstrated in a series of field experiments and virtual experiments in a MOOS-IvP simulation.United States. Office of Naval Research (Grant N00014-08-1-0011)United States. Office of Naval Research (Grant N00014-08-1-0013)NATO Undersea Research Centre (NURC

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice