Simultaneous Localization And Mapping Applications Research Articles

HECTORSLAM 2D MAPPING FOR SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)

This paper presents an application of LiDAR sensor for 2D mapping construction in an unkwown environments and capability to localize its own location based on landmark detected. Previously, there are various research actively conducted by others researchers for SLAM application. In general, it can be categorized based on three different type of sensor measurement and technique such as Vision based SLAM, RGB-D based SLAM and also Laser based SLAM. The main focus in this project is to present an experiment result conducted of a Simultaneous Localization and Mapping (SLAM) application based on laser sensor which is LiDAR in term of capability of mapping construction and localization it self. LiDAR sensor is put on the vehicle that will operate in real-world environments and computational processing done by using Robotic Operating System (ROS). This project is tested and verified in a curtain room with several parameter by using Robot Operating System (ROS). SLAM was implemented to provide localization estimates in environments, where there are static landmarks that are only rarely recognized by the vehicle or robot. This project also consider the features that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks. As conclusion, performance of SLAM by using LiDAR sensor can be apply for several robotic system such as flight control, obstacle avoidance, navigation and other function in the future application.

On the Efficiency of SLAM Using Adaptive Unscented Kalman Filter

In this paper, an adaptive unscented Kalman filter (AUKF) algorithm is applied to simultaneous localization and mapping (SLAM), based on adaptation of a scaling parameter. The scaling parameter is a design parameter in the unscented Kalman filter (UKF) which can improve the quality of the approximation. An adaptive method is designed to find the suitable value for the scaling parameter to improve the accuracy of estimation. It is demonstrated that the proposed methodology significantly reduces the state estimation error and improves the navigation accuracy of an autonomous vehicle. Also, it is highlighted that the computational cost is not much affected by increasing the number of observations, especially in the SLAM application in which the number of landmarks is growing through estimation. A comparison between UKF and AUKF algorithms is also provided for the SLAM application. The efficiency and the robustness of the proposed algorithm are investigated by applying noise of different orders in simulation results. In addition, non-credibility indices are used to compare the relative performance of AUKF and UKF. The results illustrate that AUKF-SLAM is more accurate than UKF-SLAM.

An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation.

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

AbstractThis paper presents a real‐time and channel‐invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model‐based (extensive) enhancement and a model‐free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real‐world color and grayscale underwater images. Real‐world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB‐SLAM2), a direct (LSD‐SLAM), and a visual underwater SLAM application.

On the behaviour of low cost laser scanners in HW/SW particle filter SLAM applications

Particle filters (PFs) are computationally intensive sequential Monte Carlo estimation methods with applications in the field of mobile robotics for performing tasks such as tracking, simultaneous localization and mapping (SLAM) and navigation, by dealing with the uncertainties and/or noise generated by the sensors as well as with the intrinsic uncertainties of the environment. However, the application of PFs with an important number of particles has traditionally been difficult to implement in real-time applications due to the huge number of operations they require. This work presents a hardware implementation on FPGA (field programmable gate arrays) of a PF applied to SLAM which aims to accelerate the execution time of the PF algorithm with moderate resource. The presented system is evaluated for different sensors including a low cost Neato XV-11 laser scanner sensor. First the system is validated by post processing data provided by a realistic simulation of a differential robot, equipped with a hacked Neato XV-11 laser scanner, that navigates in the Robot@Factory competition maze. The robot was simulated using SimTwo, which is a realistic simulation software that can support several types of robots. The simulator provides the robot ground truth, odometry and the laser scanner data. Then the proposed solution is further validated on standard laser scanner sensors in complex environments. The results achieved from this study confirmed the possible use of low cost laser scanner for different robotics applications which benefits in several aspects due to its cost and the increased speed provided by the SLAM algorithm running on FPGA.

Robust Range-Only SLAM for Unmanned Aerial Systems

Range-only sensors, in contrast to bearing-only sensors or LIDAR sensors are sensors which only provides distance between two points but not bearing information. The reduced amount of information provided by this sensors makes their application in Simultaneous Localization and Mapping (SLAM) problems specially challenging due to the lack of information. The complexity of the problem becomes higher when using noisy range-only sensors like radio-based where the multi-path effect may introduce range outliers, hence reducing the precision of the SLAM solution. Furthermore, the application of Range-only SLAM to aerial systems implies a higher level of complexity due to the introduction to a 3th dimensionality in contrast to ground mobile robots where the altitude dimension is not necessary in most applications. This paper presents an efficient and robust method to the Range-only SLAM (RO-SLAM) problem when applied to aerial vehicles and radio-based range-only measurements. The method is based on a pre-filtering algorithm which is able to detect and remove range outliers and on the application of a reduced parameterization which scales better with 3D RO-SLAM problems. The method is validated with real experiments involving an aerial vehicle and multiple radio-based range-only sensors.

Implementation of FastSLAM2.0 on an Embedded System and HIL Validation using Different Sensors Data

The improved particle filter based simultaneous localization and mapping (SLAM) has been developed for many robotic applications. The main purpose of this article is to demonstrate that recent heterogeneous architectures can be used to implement the FastSLAM2.0 and can greatly help to design embedded systems based robot applications and autonomous navigation. The algorithm is studied, optimized and evaluated with a real dataset using different sensors data and a hardware in the loop (HIL) method. Authors have implemented the algorithm on a system based embedded applications. Results demonstrate that an optimized FastSLAM2.0 algorithm provides a consistent localization according to a reference. Such systems are suitable for real time SLAM applications.

Study on the ORB algorithm in the application of Monocular SLAM

In order to reduce the accumulative errors in our monocular SLAM (simultaneous localization and mapping), the loop closing (detection + correction) method based on PTAM (Parallel Tracking and Mapping) is applied. Here natural environment features are necessary to be extracted efficiently, so the ORB (Oriented FAST and Rotated BRIEF (Binary Robust Independent Elementary Features)) algorithm is used for the feature extraction and matching. The experiment results show that there is strong feature recognition power in ORB so that it can realize environment recognition under the conditions of severe viewpoint change. Moreover, it is so fast to extract and match (without using multi-threading or GPU (Graphics Processing Unit) acceleration) that it can accurately track and map in real time. Its capability of fast and efficiency is verified with outdoor scene experiments.

Generic Node Removal for Factor-Graph SLAM

This paper reports on a generic factor-based method for node removal in factor-graph simultaneous localization and mapping (SLAM), which we call generic linear constraints (GLCs). The need for a generic node removal tool is motivated by long-term SLAM applications, whereby nodes are removed in order to control the computational cost of graph optimization. GLC is able to produce a new set of linearized factors over the elimination clique that can represent either the true marginalization (i.e., dense GLC) or a sparse approximation of the true marginalization using a ChowLiu tree (i.e., sparse GLC). The proposed algorithm improves upon commonly used methods in two key ways: First, it is not limited to graphs with strictly full-state relative-pose factors and works equally well with other low-rank factors, such as those produced by monocular vision. Second, the new factors are produced in such a way that accounts for measurement correlation, which is a problem encountered in other methods that rely strictly upon pairwise measurement composition. We evaluate the proposed method over multiple real-world SLAM graphs and show that it outperforms other recently proposed methods in terms of Kullback-Leibler divergence. Additionally, we experimentally demonstrate that the proposed GLC method provides a principled and flexible tool to control the computational complexity of long-term graph SLAM, with results shown for 34.9 h of real-world indoor-outdoor data covering 147.4 km collected over 27 mapping sessions spanning a period of 15 months.

A method to convert floating to fixed-point EKF-SLAM for embedded robotics

Abstract The Extended Kalman Filter (EKF) is one of the most efficient algorithms to address the problem of Simultaneous Localization And Mapping (SLAM) in the area of autonomous mobile robots. The EKF simultaneously estimates a model of the environment (map) and the position of a robot based on sensor information. The EKF for SLAM is usually implemented using floating-point data representation demanding high computational processing power, mainly when the processing is performed online during the environment exploration. In this paper, we propose a method to automatically estimate the bit-range of the EKF variables to mitigate its implementation using only fixed-point representation. In this method is presented a model to monitor the algorithm stability, a procedure to compute the bit range of each variable and a first effort to analyze the maximum acceptable system error. The proposed system can be applied to reduce the overall system cost and power consumption, specially in SLAM applications for embedded mobile robots.

Enhanced SLAM for a mobile robot using extended Kalman Filter and neural networks

This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) scheme for a mobile robot to compensate for the Extended Kalman Filter (EKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Radial Basis Function (RBF) and EKF which is a milestone for SLAM applications. A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. A probabilistic approach has dominated the solution to the SLAM problem, which is a fundamental requirement for mobile robot navigation. The proposed approach, based on a Hybrid filter, has some advantages in handling a robotic system with nonlinear dynamics because of the learning property of the neural networks. The simulation and experimental results show the effectiveness of the proposed algorithm comparing with an EKF based SLAM and Multi Layer Perceptron (MLP) method.