Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors

When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use of the MINS path, observes and maps the environment with a LIDAR unit. The MINS FastSLAM algorithm closes a 140 meter loop with a path error that remains within 1 meter of surveyed truth. This path reduces the error 79% from an odometry FastSLAM output and uses 30% of the particles

Multiple Integrated Navigation Sensors for Improving Occupancy Grid FastSLAM

An autonomous vehicle must accurately observe its location within the environment to interact with objects and accomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundings while using it to maintain an accurate location. Such a vehicle is faced with the circularly defined Simultaneous Localization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicle exploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solution to integrate stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The implementation combines the MINS path with LIDAR to observe and map the environment using the FastSLAM algorithm. In real-world tests, a mobile ground vehicle equipped with these sensors completed a 140 meter loop around indoor hallways. This SLAM solution produces a path that closes the loop and remains within 1 meter of truth, reducing the error 92% from an image-inertial navigation system and 79% from odometry FastSLAM

Multi-Robot FastSLAM for Large Domains

For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Localization and Mapping from Shore Contours and Depth

This work examines the problem of solving SLAM in aquatic environments using an unmanned surface vessel under conditions that restrict global knowledge of the robot's pose. These conditions refer specifically to the absence of a global positioning system to estimate position, a poor vehicle motion model, and absence of magnetic field to estimate absolute heading. These conditions are present in terrestrial environments where GPS satellite reception is occluded by surrounding structures and magnetic inference affects compass measurements. Similar conditions are anticipated in extra-terrestrial environments such as on Titan which lacks the infrastructure necessary for traditional positioning sensors and the unstable magnetic core renders compasses useless. This work develops a solution to the SLAM problem that utilizes shore features coupled with information about the depth of the water column. The approach is validated experimentally using an autonomous surface vehicle utilizing omnidirectional video and SONAR, results are compared to GPS ground truth

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

An Experimental Comparison of Monocular and Stereo Visual FastSLAM Implementations

Implementazione e confronto di FastSLAM monoculare e stereo evidenziando le differenti performances riguardo a mappatura e stima della traiettoria. E' inoltre proposto un algoritmo integrante Visual Odometry e SLAM da stereovisione per robot dotati di sole fotocamere come sensoriope

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