Benchmarking and Comparing Popular Visual SLAM Algorithms

This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset selected has a large set of image sequences from a Microsoft Kinect RGB-D sensor with highly accurate and time-synchronized ground truth poses from a motion capture system. The test sequences selected depict a variety of problems and camera motions faced by Simultaneous Localization and Mapping (SLAM) algorithms for the purpose of testing the robustness of the algorithms in different situations. The evaluation metrics used for the comparison are Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis involves comparing the Root Mean Square Error (RMSE) of the two metrics and the processing time for each algorithm. This paper serves as an important aid in the selection of SLAM algorithm for different scenes and camera motions. The analysis helps to realize the limitations of both SLAM methods. This paper also points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure

Visual SLAM using straight lines

The present thesis is focuses on the problem of Simultaneous Localisation and Mapping (SLAM) using only visual data (VSLAM). This means to concurrently estimate the position of a moving camera and to create a consistent map of the environment. Since implementing a whole VSLAM system is out of the scope of a degree thesis, the main aim is to improve an existing visual SLAM system by complementing the commonly used point features with straight line primitives. This enables more accurate localization in environments with few feature points, like corridors. As a foundation for the project, ScaViSLAM by Strasdat et al. is used, which is a state-of-the-art real-time visual SLAM framework. Since it currently only supports Stereo and RGB-D systems, implementing a Monocular approach will be researched as well as an integration of it as a ROS package in order to deploy it on a mobile robot. For the experimental results, the Care-O-bot service robot developed by Fraunhofer IPA will be used

DELIBOT WITH SLAM IMPLEMENTATION

This paper describes and discusses a research work on "DeliBOT – A Mobile Robot with Implementation of SLAM utilizing Computer Vision/Machine Learning Techniques". The principle objective is to study about the utilization of Kinect in mobile robotics and use it to assemble an integrated system framework equipped for building a map of environment, and localizing mobile robot with respect to the map using visual cues. There were four principle work stages. The initial step was studying and testing solutions for mapping and navigation with a RGB-D sensor, the Kinect. The accompanying stage was implementing a system framework equipped for identifying and localizing objects from the point cloud given by the Kinect, permitting the execution of further errands on the system framework, i.e. considering the computational load. The third step was identifying the landmarks and the improvement they can present in the framework. At last, the joining of the previous modules was led and experimental evaluation and validation of the integrated system. The demand of substitution of human by a robot is winding up noticeably more probable eager these days because of the likelihood of less mistakes that the robot apparently makes. Amid the previous couple of years, the technology turn out to be more accurate and legitimate outcomes with less errors, and researches started to consolidate more sensors. By utilizing accessible sensors, robot will perceive and identify environment it is in and makes map. Additionally, robot will have element of itself locating inside environment. Robot fundamental operations are identification of objects and localization for conduction of the services. Robot conduct appropriate path planning and avoidance of object by setting a target or determining goal [1]. Because of the outstanding research and robotics applications in almost every segments of life of human's, from space surveillance to health-care, solution is created for autonomous mobile robots direct tasks excluding human intervention in indoor environment [2], a few applications like cleaning facilities and transportation fields. Robot navigation in environment that is safe that performs profoundly, require environment map. Since in the greater part of applications in real-life map is not given, exploration algorithm is used

A primer on autonomous aerial vehicle design

There is a large amount of research currently being done on autonomous micro-aerial vehicles (MAV), such as quadrotor helicopters or quadcopters. The ability to create a working autonomousMAV depends mainly on integrating a simultaneous localization and mapping (SLAM) solution with the rest of the system. This paper provides an introduction for creating an autonomous MAV for enclosed environments, aimed at students and professionals alike. The standard autonomous system and MAV automation are discussed, while we focus on the core concepts of SLAM systems and trajectory planning algorithms. The advantages and disadvantages of using remote processing are evaluated, and recommendations are made regarding the viability of on-board processing. Recommendations are made regarding best practices to serve as a guideline for aspirant MAV designers.H.H.G. Coppejans performed this work as part of his Master’s degree in Computer Engineering, under the supervision of H.C. Myburgh. This work is the combination of three research assignments in the form of an exam assignment. Each assignment was thoroughly reviewed and graded by H.C. Myburgh, who also provided detailed feedback, which H.H.G Coppejans incorporated in the final draft.http://www.mdpi.com/journal/sensorsam201

Motion Planning of Intelligent Robots

Robotics is a fast growing industry that is used in everyday life. One of the most popular is intelligent mobile robots that are used for basic conventional use. The purpose of this project is to use the Turtlebot 2 to map and navigate its environment, while avoiding obstacles. Also to incorporate human machine interaction by using gesture control. This report details the research, setup, and programming process of the robot

Corrective Gradient Refinement for Mobile Robot Localization

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles

Constructing informative Bayesian priors to improve SLAM map quality

The problem of Simultaneous Localisation And Mapping (SLAM) has been widely researched and has been of particular interest in recent years, with robots and self driving cars becoming ubiquitous. SLAM solutions to date have aimed to produce faster, more robust solutions that yield consistent maps by improving the filtering algorithms used, introducing better sensors, more efficient map representations or improved motion estimates. Whilst performing well in simplified scenarios, many of these solutions perform poorly in challenging real life scenarios. It is therefore important to produce SLAM solutions that can perform well even when using limited computational resources and performing a quick exploration for time critical operations such as Urban Search And Rescue missions. In order to address this problem this thesis proposes the construction of informative Bayesian priors to improve performance without adding to the computational complexity of the SLAM algorithm. Indoors occupancy grid SLAM is used as a case study to demonstrate this concept and architectural drawings are used as a source of prior information. The use of prior information to improve the performance of robotics systems has been successful in applications such as visual odometry, self-driving car navigation and object recognition. However, none of these solutions leverage prior information to construct Bayesian priors that can be used in recursive map estimation. This thesis addresses this problem and proposes a novel method to process architectural drawings and floor plans to extract structural information. A study is then conducted to identify optimal prior values of occupancy to assign to extracted walls and empty space. A novel approach is proposed to assess the quality of maps produced using different priors and a multi-objective optimisation is used to identify Pareto optimal values. The proposed informative priors are found to perform better than the commonly used non-informative prior, yielding an increase of over 20% in the F2 metric, without adding to the computational complexity of the SLAM algorithm

WALRUS Rover Expansion

The WALRUS rover is a capable search and discovery platform aid in disaster relief. It utilizes actuated pods, onboard cameras, and aquatic mobility to provide responders with the information they need. The goal of this project is to enhance the WALRUS rover, by improving the situational awareness of the users. We utilized 3D mapping to present the environment in a natural way. We fabricated a new water resistant mast, to provide a superior view point. Finally, we implemented obstacle avoidance to allow the user to focus on the task at hand, instead of the obstacles. This document outlines the requirements and design to implement these features

Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping

The problem of simultaneous localisation and mapping (SLAM) has been addressed in numerous ways with different approaches aiming to produce faster, more robust solutions that yield consistent maps. This focus, however, has resulted in a number of solutions that perform poorly in challenging real life scenarios. In order to achieve improved performance and map quality this article proposes a novel method to construct informative Bayesian mapping priors through a multi-objective optimisation of prior map design variables defined using a source of prior information. This concept is explored for 2D occupancy grid SLAM, constructing such priors by extracting structural information from architectural drawings and identifying optimised prior values to assign to detected walls and empty space. Using the proposed method a contextual optimised prior can be constructed. This prior is found to yield better quantitative and qualitative performance than the commonly used non-informative prior, yielding an increase of over 20% in the F2 metric. This is achieved without adding to the computational complexity of the SLAM algorithm, making it a good fit for time critical real life applications such as search and rescue missions