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Localisation and mapping are fundamental capabilities for autonomous mobile robots, and there has been a large amount of recent work in these fields. However, much of the work does not consider dynamic environments that include humans and moving objects. Such objects can cause occlusions resulting in a fewer visible landmarks, which can decrease localisation performance. This paper describes a novel...
This manuscript presents an autonomous navigation of a mobile robot using SLAM, while relying on an active stereo vision. We show a framework of low-level software coding which is necessary when the vision is used for multiple purposes such as obstacle discovery. The built system incorporated a number of SLAM based routines while replying on stereo vision mechanism. The system was implemented and...
This paper presents a non-iterative pose-graph optimization algorithm for fast 2D simultaneous localization and mapping (SLAM). The graph-SLAM approach addresses the SLAM problem using a factor graph structure. For a pose-only SLAM problem, landmarks are not explicitly modeled and are not a part of the SLAM problem. Conventional pose-graph optimization methods minimize the error by an iterative local...
This paper proposes a real-time nonlinear filtering approach for the SLAM problem, termed as compressed Unscented Kalman filter (CUKF). A partial sampling strategy was recently proposed to make the computational complexity of the UKF quadratic with the state-vector dimension. However, the quadratic complexity remains intractable for the large-scale SLAM. To address this problem, we firstly prove the...
Simultaneous localization and mapping, drivability classification of the terrain and path planning represent three major research areas in the field of autonomous outdoor robotics. Especially unstructured environments require a careful examination as they are unknown, continuous and the number of possible actions for the robot are infinite. We present an approach to create a semantic 3D map with drivability...
Automatically self-localization of home service robot in indoor environments is a key issue with high efficiency and robustness. State-of-the-art frameworks in computer vision typically are based on Simultaneous Localization And Mapping (SLAM) and extended version such as Ceiling Vision SLAM (CV-SLAM). However, a large amount of map information about landmarks in the ceiling are redundant for home...
In order to solve the stability of mapping of mobile robot, a fusion method based on Kalman Filter is proposed to reduce the accumulative errors during the mobile motion. This fusion method, which can fuse the sequential scan matching results and odometer measures, is suitable for raw points based scan matching method. In this paper, pose estimation results from raw points based scan matching method...
Magnetic guidance is a reliable navigation method for intelligence vehicles and AGVs. To get the right and accurate location of vehicle for control, an accurate magnet map is needed. This paper proposed a new approach to build a magnet map by graph-based SLAM. Vehicle's poses in the driving trajectory can be estimated by SLAM, for the magnetic sensor installed on the vehicle is detecting the relative...
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