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

Simultaneous Mapping and Localisation for Small Military Unmanned Underwater Vehicle

Paper proposes a simultaneous localisation and mapping (SLAM) scheme which is applicable to small military unmanned underwater vehicles (UUVs). The SLAM is a process which enables concurrent estimation of the position of UUV and landmarks in the environment through which the vehicle is passing. An unscented Kalman filter (UKF) is utilised to develop a SLAM suitable to nonlinear motion of UUV. A range sonar is used as a sensor to collect the relative position information of the landmark in the environment in which the UUV is navigating. The proposed SLAM scheme was validated through towing tank experiments about two degrees of freedom motion with UUV motion simulator and real range sonar system for small UUV. The results of these experiments showed that proposed SLAM scheme is capable of estimating the position of the UUV and the surrounding objects under real underwater environment.Defence Science Journal, 2012, 62(4), pp.223-227, DOI:http://dx.doi.org/10.14429/dsj.62.100

A practical method for implementing an attitude and heading reference system

This paper describes a practical and reliable algorithm for implementing an Attitude and Heading Reference System (AHRS). This kind of system is essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms are required for performance to be acceptable. The proposed method is based on an Extended Kalman Filter (EKF) in a direct configuration. In this case, the filter is explicitly derived from both the kinematic and rror models. The selection of this kind of EKF configuration can help in ensuring a tight integration of the method for its use in filter-based localization and mapping systems in autonomous vehicles. Experiments with real data show that the proposed method is able to maintain an accurate and drift-free attitude and heading estimation. An additional result is to show that there is no ostensible reason for preferring that the filter have an indirect configuration over a direct configuration for implementing an AHRS system.Postprint (published version

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Range-only SLAM schemes exploiting robot-sensor network cooperation

Simultaneous localization and mapping (SLAM) is a key problem in robotics. A robot with no previous knowledge of the environment builds a map of this environment and localizes itself in that map. Range-only SLAM is a particularization of the SLAM problem which only uses the information provided by range sensors. This PhD Thesis describes the design, integration, evaluation and validation of a set of schemes for accurate and e_cient range-only simultaneous localization and mapping exploiting the cooperation between robots and sensor networks. This PhD Thesis proposes a general architecture for range-only simultaneous localization and mapping (RO-SLAM) with cooperation between robots and sensor networks. The adopted architecture has two main characteristics. First, it exploits the sensing, computational and communication capabilities of sensor network nodes. Both, the robot and the beacons actively participate in the execution of the RO-SLAM _lter. Second, it integrates not only robot-beacon measurements but also range measurements between two di_erent beacons, the so-called inter-beacon measurements. Most reported RO-SLAM methods are executed in a centralized manner in the robot. In these methods all tasks in RO-SLAM are executed in the robot, including measurement gathering, integration of measurements in RO-SLAM and the Prediction stage. These fully centralized RO-SLAM methods require high computational burden in the robot and have very poor scalability. This PhD Thesis proposes three di_erent schemes that works under the aforementioned architecture. These schemes exploit the advantages of cooperation between robots and sensor networks and intend to minimize the drawbacks of this cooperation. The _rst scheme proposed in this PhD Thesis is a RO-SLAM scheme with dynamically con_gurable measurement gathering. Integrating inter-beacon measurements in RO-SLAM signi_cantly improves map estimation but involves high consumption of resources, such as the energy required to gather and transmit measurements, the bandwidth required by the measurement collection protocol and the computational burden necessary to integrate the larger number of measurements. The objective of this scheme is to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a centralized mechanism running in the robot that adapts measurement gathering. The second scheme of this PhD Thesis consists in a distributed RO-SLAM scheme based on the Sparse Extended Information Filter (SEIF). This scheme reduces the increment in resource consumption resulting from the integration of inter-beacon measurements by adopting a distributed SLAM _lter in which each beacon is responsible for gathering its measurements to the robot and to other beacons and computing the SLAM Update stage in order to integrate its measurements in SLAM. Moreover, it inherits the scalability of the SEIF. The third scheme of this PhD Thesis is a resource-constrained RO-SLAM scheme based on the distributed SEIF previously presented. This scheme includes the two mechanisms developed in the previous contributions {measurement gathering control and distribution of RO-SLAM Update stage between beacons{ in order to reduce the increment in resource consumption resulting from the integration of inter-beacon measurements. This scheme exploits robot-beacon cooperation to improve SLAM accuracy and e_ciency while meeting a given resource consumption bound. The resource consumption bound is expressed in terms of the maximum number of measurements that can be integrated in SLAM per iteration. The sensing channel capacity used, the beacon energy consumed or the computational capacity employed, among others, are proportional to the number of measurements that are gathered and integrated in SLAM. The performance of the proposed schemes have been analyzed and compared with each other and with existing works. The proposed schemes are validated in real experiments with aerial robots. This PhD Thesis proves that the cooperation between robots and sensor networks provides many advantages to solve the RO-SLAM problem. Resource consumption is an important constraint in sensor networks. The proposed architecture allows the exploitation of the cooperation advantages. On the other hand, the proposed schemes give solutions to the resource limitation without degrading performance

Robust state estimation methods for robotics applications

State estimation is an integral component of any autonomous robotic system. Finding the correct position, velocity, and orientation of an agent in its environment enables it to do other tasks like mapping and interacting with the environment, and collaborating with other agents. State estimation is achieved by using data obtained from multiple sensors and fusing them in a probabilistic framework. These include inertial data from Inertial Measurement Unit (IMU), images from camera, range data from lidars, and positioning data from Global Navigation Satellite Systems (GNSS) receivers. The main challenge faced in sensor-based state estimation is the presence of noisy, erroneous, and even lack of informative data. Some common examples of such situations include wrong feature matching between images or point clouds, false loop-closures due to perceptual aliasing (different places that look similar can confuse the robot), presence of dynamic objects in the environment (odometry algorithms assume a static environment), multipath errors for GNSS (signals for satellites jumping off tall structures like buildings before reaching receivers) and more. This work studies existing and new ways of how standard estimation algorithms like the Kalman filter and factor graphs can be made robust to such adverse conditions without losing performance in ideal outlier-free conditions. The first part of this work demonstrates the importance of robust Kalman filters on wheel-inertial odometry for high-slip terrain. Next, inertial data is integrated into GNSS factor graphs to improve the accuracy and robustness of GNSS factor graphs. Lastly, a combined framework for improving the robustness of non-linear least squares and estimating the inlier noise threshold is proposed and tested with point cloud registration and lidar-inertial odometry algorithms followed by an algorithmic analysis of optimizing generalized robust cost functions with factor graphs for GNSS positioning problem

Joint Localization Based on Split Covariance Intersection on the Lie Group

This paper presents a pose fusion method that accounts for the possible correlations among measurements. The proposed method can handle data fusion problems whose uncertainty has both independent part and dependent part. Different from the existing methods, the uncertainties of the various states or measurements are modeled on the Lie algebra and projected to the manifold through the exponential map, which is more precise than that modeled in the vector space. The dealing of the correlation is based on the theory of covariance intersection, where the independent and dependent parts are split to yield a more consistent result. In this paper, we provide a novel method for correlated pose fusion algorithm on the manifold. Theoretical derivation and analysis are detailed first, and then the experimental results are presented to support the proposed theory. The main contributions are threefold: (1) We provide a theoretical foundation for the split covariance intersection filter performed on the manifold, where the uncertainty is associated on the Lie algebra. (2) The proposed method gives an explicit fusion formalism on SE(3) and SE(2), which covers the most use cases in the field of robotics. (3) We present a localization framework that can work both for single robot and multi-robots systems, where not only the fusion with possible correlation is derived on the manifold, the state evolution and relative pose computation are also performed on the manifold. Experimental results validate its advantage over state-of-the-art methods