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 and consider future directions. 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-27i1pn9khc.pdf

Past, Present, and Future of Simultaneous Localization and Mapping: Toward 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?

/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

A survey on coverage path planning for robotics

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

/pdf/auv-navigation-and-localization-a-review-2aaowbjv0i.pdf

AUV Navigation and Localization: A Review

Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.

/pdf/semantickitti-a-dataset-for-semantic-scene-understanding-of-51tckxzy5f.pdf

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Simultaneous localization and mapping SLAM in unknown GPS-denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state-of-the-art multiple-robot systems, with a major focus on multiple-robot SLAM. Various issues and problems in multiple-robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

/pdf/multiple-robot-simultaneous-localization-and-mapping-a-qeqtr8xeje.pdf

Multiple-Robot Simultaneous Localization and Mapping: A Review

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.

http://people.csail.mit.edu/lpaull/publications/Paull_TMECH_2012.pdf

Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles

This paper presents the development of a nonlinear quadrotor simulation framework together with a nonlinear controller. The quadrotor stabilization and navigation problems are tackled using a nested loops control architecture. A nonlinear Backstepping controller is implemented for the inner stabilization loop. It asymptotically tracks reference attitude, altitude and heading trajectories. The outer loop controller generates the reference trajectories for the inner loop controller to reach the desired waypoint. To ensure boundedness of the reference trajectories, a PD controller with a saturation function is used for the outer loop. Due to the complexity involved in controller development and testing, a simulation framework has been developed. It is based on the Gazebo 3D robotics simulator and the Open Dynamics Engine (ODE) library. The framework can effectively facilitate the development and validation of controllers. It has been released and is available at Gazebo quadrotor simulator (2012).

/pdf/control-and-navigation-framework-for-quadrotor-helicopters-16pgnizbc7.pdf

Control and Navigation Framework for Quadrotor Helicopters

Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations or measurements. Here, we present a dataset with the aim of providing a higher degree of photo-realism, larger scale, more variability as well as serving a wider range of purposes compared to existing datasets. Our dataset leverages the availability of millions of professional interior designs and millions of production-level furniture and object assets -- all coming with fine geometric details and high-resolution texture. We render high-resolution and high frame-rate video sequences following realistic trajectories while supporting various camera types as well as providing inertial measurements. Together with the release of the dataset, we will make executable program of our interactive simulator software as well as our renderer available at this https URL. To showcase the usability and uniqueness of our dataset, we show benchmarking results of both sparse and dense SLAM algorithms.

Sajad Saeedi

Papers

AUV Navigation and Localization: A Review

Multiple-Robot Simultaneous Localization and Mapping: A Review

Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles

Control and Navigation Framework for Quadrotor Helicopters

InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset