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In this paper, we describe a system that can carry out visual simultaneous localization and mapping (VSLAM) in structured indoor environments; this system uses a small-sized low-cost commercial humanoid robot which uses a camera mounted on a pan-and-tilt device (it was designed and built by us) as his head. Almost all of the VSLAM approaches use points 2D like features of the robot's environment for...
This paper describes a GA approach for solving the SLAM problem. We treat the local scan from laser sensor as an image pattern. Two subsequent scans were matched to find the robot's ego motion. This motion is used as updates for robot's global position estimation in the map. A virtual scan is obtained from the map in the robot's current position. This virtual scan is then matched with the current...
This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation...
Global localization aims to estimate a robot's pose in a learned map without any prior knowledge of its initial pose. Achieving highly accurate global localization remains a challenge for autonomous mobile robots especially in large-scale unstructured outdoor environments. This paper introduces a real-time reliable global localization approach with the capability of addressing the kidnapped robot...
This paper deals with the autonomous navigation scheme for a mobile robot in indoor environment using an upward-looking camera and sonar sensors. Corner and lamp features are extracted from the sequential ceiling images, and these features are used as landmarks in the SLAM (simultaneous localization and mapping) process. Combining lamp information with the conventional corner feature-based approach...
This paper presents a method for SLAM in Robot and Wireless Sensor Network (WSN) System using Bayes framework. A mobile robot equipped with sensor measuring the range to WSN nodes by Received Signal Strength Indicator (RSSI) can simultaneously localize itself as well as locate the sensor nodes of WSN. We adopted an algorithm based Rao-Blackwellized Particle Filter (RBPF) to integrate measurements...
This paper presents a method of 3D localization using image edge-points detected from binocular stereo image sequences. The proposed method calculates camera poses using visual odometry, and updates the poses by reducing the accumulated errors using landmark recognition. Landmark recognition is done based on robust and scalable image-retrieval using image edge-points with SIFT descriptors and a vocabulary...
This paper presents a statistically consistent SLAM algorithm where the environment is represented using a collection of B-Splines. The use of B-Splines allow environment to be represented without having to extract specific geometric features such as lines or points. Our previous work proposed a new observation model that enables raw measurements taken from a laser range finder to be transferred into...
We present a novel “hybrid Hessian” six-degrees-of-freedom simultaneous localization and mapping (SLAM) algorithm. Our method allows for the smooth trade-off of accuracy for efficiency and for the incorporation of GPS measurements during real-time operation, thereby offering significant advantages over other SLAM solvers. Like other stochastic SLAM methods, such as SGD and TORO, our technique is robust...
In recent SLAM (simultaneous localization and mapping) literature, Pose Only optimization methods have become increasingly popular. This is greatly supported by the fact that these algorithms are computationally more efficient, as they focus more on the robots trajectory rather than dealing with a complex map. Implementation simplicity allows these to handle both 2D and 3D environments with ease....
Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically...
We present a graph-based SLAM approach, using monocular vision and odometry, designed to operate on computationally constrained platforms. When computation and memory are limited, visual tracking becomes difficult or impossible, and map representation and update costs must remain low. Our system constructs a map of structured views using only weak temporal assumptions, and performs recognition and...
A vision based exploration algorithm that invokes semantic cues for constructing a hybrid map of images - a combination of semantic and topological maps is presented in this paper. At the top level the map is a graph of semantic constructs. Each node in the graph is a semantic construct or label such as a room or a corridor, the edge represented by a transition region such as a doorway that links...
This paper is about online, constant-time pose estimation for road vehicles. We exploit both the state of the art in vision based SLAM and the wide availability of overhead imagery of road networks. We show that by formulating the pose estimation problem in a relative sense, we can estimate the vehicle pose in real-time and bound its absolute error by using overhead image priors. We demonstrate our...
In this paper we propose a simultaneous localization and mapping (SLAM) method that utilizes local anomalies of the ambient magnetic field present in many indoor environments. We use a Rao-Blackwellized particle filter to estimate the pose distribution of the robot and Gaussian Process regression to model the magnetic field map. The feasibility of the proposed approach is validated by real world experiments,...
Visual maps of the seafloor should ideally provide the ability to measure individual features of interest in real units. Two-dimensional photomosaics cannot provide this capability without making assumptions that often fail over 3-D terrain, and are generally used for visualization, but not for measurement. Full 3-D structure can be recovered using stereo vision, structure from motion (SFM), or simultaneous...
This paper presents a new method of localization and map building of mobile robot based on mixed map model using LRF (Laser Range Finder). The mixed model composed of occupancy grids and line character maps is utilized to represent the environment map. Firstly, the LRF models and Bayes rules are used to construct a local occupancy grid map. Then, we extract obstacles points to get a precise geometry...
A localisation system is an essential knowledge for a mobile robot to be able to freely navigate in its world. In this paper, pose estimation and tracking of a mobile robot is presented for an indoor cluttered environment using only an overhead panoramic vision system. The method presented is fast without requiring unwrapping of the panoramic view. It is assumed that the robot's workspace is 2D planer...
We present a head-mounted, stereo-vision based navigational assistance device for the visually impaired. The head-mounted design enables our subjects to stand and scan the scene for integrating wide-field information, compared to shoulder or waist-mounted designs in literature which require body rotations. In order to extract and maintain orientation information for creating a sense of egocentricity...
This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The...
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