Lidar-Based 2D SLAM for Mobile Robot in an Indoor Environment: A Review

In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.

The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of five open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized.

To navigate successfully, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A SLAM (Simultaneous Localization and Mapping) system employs its sensor data to build incrementally a map of an unknown environment and to localize itself in the map simultaneously. Thanks to recent advances in computer vision and cheaper cameras, vision sensors have become popular to solve SLAM problems. The proposed bearing-only SLAM system requires only a single camera which is simple and affordable for the navigation of domestic robots such as autonomous lawn-mowers and vacuum cleaners. Existing approaches to bearing-only SLAM require the readings from an odometer to estimate the robot locations prior to landmark initialization. This chapter presents a new 2-dimensional bearing-only SLAM system that relies only on the bearing measurements from a single camera. Our proposed system does not require any other sensors like range sensors or wheel encoders. The trade-off is that it requires the robot to be able to move in a straight line for a short while to initialize the landmarks. Once the landmark positions are estimated, localization becomes easy. The induced map created by our method is only determined up to a scale factor as only bearing information is used (no range or odometry information). All the object coordinates in the map multiplied by a scale factor would not change the bearing values.

Applications such as Simultaneous Localization and Mapping (SLAM) can greatly benefit from RGB-D sensor data to produce 3D maps of the environment as well as sensor’s trajectory estimation. However, the resulting 3D points map can be cumbersome, and since indoor environments are mainly composed of planar surfaces, the idea is to use planes as building blocks for a SLAM process. This paper describes an RGB-D SLAM system benefiting from planes segmentation to generate lightweight 3D plane-based maps. Our goal is to produce reduced 3D maps composed solely of planes sections that can be used on platforms with limited memory and computation resources. We present the introduction of planar regions in a regular RGB-D SLAM system and evaluate the benefits regarding both resulting map and estimated camera trajectory.

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

Magnetic field constraints and sequence-based matching for indoor pose graph SLAM

This paper proposes a real-time RGB-D (red-green-blue depth) 3D SLAM (simultaneous localization and mapping) system. Kinect style sensors give RGB-D data which contains 2D image and per-pixel depth information. 6-DOF (degree-of-freedom) visual odometry is obtained through the 3D-RANSAC (three-dimensional random sample consensus) algorithm with image features and depth information. For speed up extraction of features, parallel computation is performed on a GPU (graphics processing unit) processor. After a feature manager detects loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and 3D map. Experimental results show the processing rate over 20 Hz.

The reference map for a given environment is one of the most essential elements for a mobile robot to automatically control itself and find a path. Simultaneous localization and mapping (SLAM) is a technology that aids the autonomous movement of a mobile robot in a given environment by simultaneously creating a map and performing localization. Graph-based SLAM is the state-of-the-art solution. However, graph-based SLAM faces certain problems when constructing graphs because of the front-end processing failure that occurs when using Iterative Closest Points (ICP). When the robot is in motion, a deviation between the scan images of the environment appears in every scan. Therefore, it is necessary to perform matching of the two images by using an outlier rejection algorithm for a robust ICP algorithm. In this paper, we propose an ICP outlier rejection scheme to compare to scan images and select matching points and reject mismatched parts. The experimental results show that the proposed scheme is reliable and robust in practical environments.

Simultaneous Localization and Mapping (SLAM) has been studied a lot since 20 years in the robotics community: a SLAM method copes with the incremental construction of a geometrical model from the successive fusion of observations acquired from moving sensors. When sensors are embedded on a mobile robot, robot localization and environment mapping must be performed in the same function. Many methods aim to build sparse representations, made of 2D segments (from 2D laser data for indoor applications) or 3D points (from a single camera or stereovision): this paper discusses some issues related to 3D and dense SLAM, i.e. the incremental construction of a textured surfacic model from the fusion of sensory data acquired from a 3D laser range finder coupled with a camera: the fusion of multisensory data in a unified 3D representation makes more robust the data association between features perceived from different viewpoints. First an heterogeneous model, made of a stochastic map and of appearance-based information, is described, before focusing on the data association function; our approach is validated by experimental results obtained from data acquired on our Jido robot.

Typical visual simultaneous localization and mapping (SLAM) systems rely on purely geometric elements. Recently semantic SLAM has become popular due to its capability to obtain high-level understanding of the environment. However, most semantic SLAM systems merely aim to produce semantic maps rather than exploiting semantic constraints to boost the accuracy performance. In this paper, we propose a new RGB-D SLAM system for the use in indoor environments. Given that a ground plane represents a significant part of most indoor scenes, our new system exploit a semantic ground plane constraint inferred from a deep neural network along with geometric constraints on the state estimation to improve the performance. More specifically, we introduce a semantically meaningful geometric primitive, namely, a global ground plane to the optimization process of visual SLAM. The global ground model is able to provide an extra constraint on the state estimation through which both frame-to-keyframe and frame-to-model tracking processes are combined, and low-texture ground plane is effectively utilized. Experimental results for the TUM dataset and a real scene have demonstrated the improved performance brought by the semantic constraint in our method.

Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map.

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

In this work, we tested Simultaneous localization and mapping (SLAM) about mobile robots in indoor environment, where all experiments were conducted based on the Robot Operating System (ROS). The urban search and rescue(USAR) environment was build in the ROS simulation tool Gazebo, and our car was used to test hector SLAM in Gazebo. The rplidar A1 single-line lidar was used for 2D laser scan matching data acquisition in the practical experiments and the indoor map was built by using the open source algorithms gmapping, karto SLAM, hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the three open source algorithms show that the mobile robot for simultaneous localization and mapping (SLAM) is feasible, and high-precision grid maps can be constructed.

A Simultaneous Localization and Mapping(SLAM) system is a complex program consisting of several interconnected components with different functionalities such as optimization, tracking or loop detection. Whereas the literature addresses in detail how enhancing the algorithmic aspects ofthe individual components improves SLAM performance, the modal aspects, such as when to localize, relocalize or close a loop, are usually left aside. In this paper, we address the modal aspects of a SLAM system and show that the design of the modal controller has a strong impact on SLAM performance in particular in terms of robustness against unforeseen events such as sensor failures, perceptual aliasing or kidnapping. We preset a novel taxonomy for the components of a modern SLAM system, investigate their interplay and propose a highly modular architecture of a generic SLAM system using the Unified Modeling LanguageTM(UML) state machine formalism. The result, called SLAM state machine, is compared to the modal controller of several state-of-the-art SLAM systems and evaluated in two experiments. We demonstrate that our state machine handles unforeseen events much more robustly than the state-of-the-art systems.

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.