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?

/pdf/past-present-and-future-of-simultaneous-localization-and-dhljw0akg5.pdf

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 representation 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. The paper serves as a tutorial for the non-expert reader. It is also a position paper: 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? Is SLAM solved?

Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age.

This paper presents an consistent decentralized data fusion approach for robust multi-robot SLAM in dangerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neighborhood information in a single, consistent augmented local map, without the overly conservative approach to avoiding information double-counting in the previous DDF-SAM algorithm. We introduce the anti-factor as a means to subtract information in graphical SLAM systems, and illustrate its use to both replace information in an incremental solver and to cancel out neighborhood information from shared summarized maps. This paper presents and compares three summarization techniques, with two exact approaches and an approximation. We evaluated the proposed system in a synthetic example and show the augmented local system and the associated summarization technique do not double-count information, while keeping performance tractable.

https://www.cc.gatech.edu/~dellaert/pubs/Cunningham13icra.pdf

DDF-SAM 2.0: Consistent distributed smoothing and mapping

In this paper, we present a Covariance Intersection (CI)-based algorithm for reducing the processing and communication complexity of multi-robot Cooperative Localization (CL). Specifically, for a team of N robots, our proposed approximate CI-based CL approach has processing and communication complexity only linear, O(N), in the number of robots. Moreover, and in contrast to alternative approximate methods, our approach is provably consistent, can handle asynchronous communication, and does not place any restriction on the robots' motion. We test the performance of our proposed approach in both simulations and experimentally, and show that it outperforms the existing linear-complexity split CI-based CL method.

/pdf/decentralized-multi-robot-cooperative-localization-using-1pnh0jl69u.pdf

Decentralized multi-robot cooperative localization using covariance intersection

We report a decentralized extended information filter (DEIF) algorithm designed for single-beacon cooperative acoustic navigation of one or more client underwater vehicles. In single-beacon cooperative acoustic navigation, ranges and state information from a single reference source (the server) are used to improve localization and navigation of an underwater vehicle (the client). The ranges and state information are obtained using underwater acoustic modems and a synchronous-clock time-of-flight paradigm. Apart from the server's acoustic data broadcasts, the client has no access to the server's position or sensor measurements. We show that at the instance of each range measurement update, the DEIF algorithm yields identical results for the current vehicle state estimate as the corresponding centralized extended information filter (CEIF), which fully tracks the joint probability distribution between the server and client. We compare the state estimation results of the DEIF algorithm with that of a CEIF and three other filters reported in the literature. The evaluation is performed using both simulated data and an experimental dataset comprised of one surface craft and two autonomous underwater vehicles.

Decentralized Extended Information Filter for Single-Beacon Cooperative Acoustic Navigation: Theory and Experiments

This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

/pdf/decentralised-cooperative-localisation-for-heterogeneous-acjmnq4lyf.pdf

Decentralised cooperative localisation for heterogeneous teams of mobile robots

This paper presents a new technique for sparsification of the information matrix of a multi-dimensional Gaussian distribution. We call this technique Conservative Sparsification (CS) and show that it produces estimates which are consistent with respect to an optimal filter. This technique was applied to the Simultaneous Localisation and Mapping (SLAM) problem, and compared with two existing sparsification approaches; the Sparse Extended Information Filter (SEIF) and the Data Discarding Sparse Extended Information Filter (DDSEIF). Simulation demonstrates that CS is a consistent approach and provides a tighter upper bound than existing conservative methods.

John Vial

Papers

Decentralised cooperative localisation for heterogeneous teams of mobile robots

Conservative Sparsification for efficient and consistent approximate estimation