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ORB-SLAM2 is one of the better-known open source SLAM implementations available. However, the dependence of visual features causes it to fail in featureless environments. With the present work, we propose a new technique to improve visual odometry results given by ORB-SLAM2 using a tightly Sensor Fusion approach to integrate camera and odometer data. In this work, we use odometer readings to improve...
In this paper, we propose a framework to map stationary sound sources while simultaneously localise a moving robot. Conventional methods for localisation and sound source mapping rely on a microphone array and either, 1) a proprioceptive sensor only (such as wheel odometry) or 2) an additional exteroceptive sensor (such as cameras or lasers) to get accurately the robot locations. Since odometry drifts...
In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated...
The ambience of research in VSLAM and relocalization algorithms in the last few years allure with real-time localization and increased precision for RGBD or stereo cameras but with ambivalence requesting higher computational capacity or more expensive sensors. The aim of this paper is to present a gentle algorithm to locate a humanoid robot using relocalization view based algorithms and odometry information...
This paper presents a multi-robot system for an intelligent wheelchair. This system creates maps of the environment using SLAM (Simultaneous Localization And Mapping) algorithm and partially updates this maps around the planned trajectory after fixing the target by the user. Temporary obstacles like humans, animals, closed doors ... Are not considered in the updating process. An object recognition...
Our work presents a proposal for SLAM (Simultaneous Localization and Mapping) problem in dynamic environments. A method for robot self-localization is described. During operations in dynamic environments, only static landmarks are used. For this purpose, an outliers filter was designed. It separates static and mobile landmarks that are included in a set of neglectable marks. A modified particle filter...
The virtual reconstruction of underwater environments in 3 dimensions can be of great utility for scientific or industrials applications, being the use of Autonomous Underwater Vehicles (AUVs) equipped with cameras an invaluable tool with progressive improvements. However, a highly accurate vehicle localization process is fundamental to place every portion of that 3D model in its real position with...
This paper presents a novel approach for multirobot pose graph localization and data association without requiring prior knowledge about the initial relative poses of the robots. Without a common reference frame, the robots can only share observations of interesting parts of the environment, and trying to match between observations from different robots will result in many outlier correspondences...
This paper proposes a novel method of integrating planning with Monocular Simultaneous Localization and Mapping (SLAM) systems. Monocular SLAM, typically referred to as VSLAM systems in literature consists of recovering trajectory estimates of the camera and stationary world features from a single moving camera. Such VSLAM systems are significantly more difficult than SLAM performed with depth sensors,...
This paper presents a vision-based Unscented FastSLAM (UFastSLAM) algorithm combing the Rao-Blackwellized particle filter and Unscented Kalman filte(UKF). The landmarks are detected by a binocular vision to integrate localization and mapping. Since such binocular vision system generally inherits larger measurement errors, it is suitable to adopt Unscented FastSLAM to improve the performance of localization...
This paper introduces a novel mapping and localization framework for mobile robots named ”co-embedding”, partly inspired by human cognitive mapping process. In this method, the spatial relationship among objects (i.e., map) and robot's trajectory are reconstructed in a bottom-up way by embedding the high-dimensional observation data into a low-dimensional space with a set of locally linear transformations...
This paper describes the design and testing of a system to enable large scale cooperative navigation of autonomous vehicles moving on a priori unknown routes in changing environments. A large-scale learning-mapping approach and a replay-localization method are combined to achieve cooperative navigation. The mapping approach is based on a proposed hierarchical/hybrid BiCam SLAM approach-global level...
We propose in the following paper to tackle the multi-robot exploration problem. Our approach does not presuppose the availability of maps of the environment. We first describe a laser based on-line map building associated with a landmark based methodology to fuse team robots observations in the general case where no relative poses are given. We then explain how to assign the next best target in case...
This paper proposes a novel approach to perform underwater Simultaneous Localization and Mapping (SLAM) using a Mechanically Scanned Imaging Sonar (MSIS). This approach starts by processing the MSIS data in order to obtain range scans while taking into account the robot motion. Then, the relative motions between consecutively gathered scans are stored in the state vector. Thus, the whole sequence...
This paper presents a novel approach to self calibrate the extrinsic parameters of a camera mounted on a mobile robot in the context of fusion with the odometry sensor. Calibrating precisely such a system can be difficult if the camera is mounted on a vehicle where the frame is difficult to localize precisely (like on a car for example). However, the knowledge of the camera pose in the robot frame...
This paper presents an approach to perform Simultaneous Localization and Mapping (SLAM) in underwater environments using a Mechanically Scanned Imaging Sonar (MSIS) not relying on the existence of features in the environment. The proposal has to deal with the particularities of the MSIS in order to obtain range scans while correcting the motion induced distortions. The SLAM algorithm manages the relative...
This paper proposes a framework to perform Simultaneous Localization and Mapping (SLAM) using the scans gathered by a Mechanically Scanned Imaging Sonar (MSIS). To this end, the acoustic profiles provided by the MSIS are processed to obtain range data. Also, dead reckoning is used to compensate the robot motion during the sonar mechanical scanning and build range scans. When a new scan is constructed,...
This paper describes a new algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization...
In the present work, a strategy to turn a car-like mobile robot in a restricted environment is presented. The strategy uses a Simultaneous Localization and Map Building (SLAM) algorithm to localize the robot in the environment and uses the map generated by the SLAM in the reverse movement of the turning strategy. The planning strategy takes into account the variance propagation in the predicted path...
This paper addresses a novel approach to the solution of the simultaneous localization and mapping (SLAM) problem bared on a neuro evolutionary optimization (NeoSLAM) method. The proposed algorithm first casts SLAM as a global optimization problem using the cost function which represents the quality of robot pose trajectory and the feature positions in world coordinate frame. In our algorithm, the...
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