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We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel...
In this paper, we present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinectv2 that is capable of reconstructing volumetric 3D map without the aid of a graphics processing unit (GPU). For many robots, including flying robots and ground mobile robots, most of them build 3D maps, such as sparse or dense point cloud. However, these maps can not give...
We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across...
Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments...
In the last decade, monocular simultaneous localization and mapping (mono-SLAM) has appeared as another alternative for pose estimation, but this last gives a localization up to scale, and suffers from scale drift due to the difficulty of depth evaluation; however, several approaches had been tackled to recover the scale and take off the ambiguity. Both methods were designed to get the accurate scale...
Simultaneous localization and mapping (SLAM) has a wide range of applications, such as mobile robots, intelligent vehicle localization, and intelligent transportation system. However, loop closure detection is a challenge task for SLAM. This task concerns the difficulty of recognizing already mapped areas. To this end, this paper proposes a novel loop closure detection method called image sequence...
Loop closure is a well-known problem in the research of laser based simultaneous localization and mapping, especially for applications in large-scale environments. The cumulative errors in the estimated pose and map make the loop detection difficult, no matter using particle filter-based or graph-based SLAM methods. Camera has the advantage of rich information but suffers from short distance and relative...
Most dense RGB/RGB-D SLAM systems require the brightness of 3-D points observed from different viewpoints to be constant. However, in reality, this assumption is difficult to meet even when the surface is Lambertian and illumination is static. One cause is that most cameras automatically tune exposure to adapt to the wide dynamic range of scene radiance, violating the brightness assumption. We describe...
Keyframe-based SLAM has achieved great success in terms of accuracy, efficiency and scalability. However, due to parallax requirement and delay of map expansion, traditional keyframe-based methods easily encounter the robustness problem in the challenging cases especially for fast motion with strong rotation. For AR applications in practice, these challenging cases are easily encountered, since a...
In this paper we propose a novel method called s-DVO for dense visual odometry using a probabilistic sensor noise model. In contrast to sparse visual odometry, where camera poses are estimated based on matched visual features, we apply dense visual odometry which makes full use of all pixel information from an RGB-D camera. Previously, t-distribution was used to model photometric and geometric errors...
In this demo, we present RKSLAM, a robust keyframe-based monocular SLAM system that can reliably handle fast motion with strong rotation and ensure good AR experiences. We contribute two key technical contributions: a novel multi-homography based feature tracking method which is very robust and efficient, and a sliding-window based camera pose optimization scheme which imposes the motion prior constraints...
In this paper, we propose a novel marker design and its tracking algorithm for room-sized MR/AR environments. The markers and the algorithm are designed to solve the following practical problems: i) the difficulties in creating and arranging markers and ii) the trade-off between inconspicuousness and robustness of markers. The proposed markers are small chips that are cut off a large paper sheet,...
In this work, we present a novel RGB-D SLAM algorithm. The novelty of the proposed algorithm lies in the use of both feature points and plane patches for pose estimation. A plane patch is defined as a small-sized patch constructed by using a feature point with small curvature. The feature points with small curvature are called plane points. The remaining feature points are classified as either smooth...
This paper introduces an object following method based on the computational geometry and PTAM for Unmanned Aerial Vehicle(UAV) in unknown environments. Since the object is easy to move out of the field of view(FOV) of the camera, and it is difficult to make it back to the field of camera view just by relative attitude control, we propose a novel solution to re-find the object based on the visual simultaneous...
The usage of a global positioning system (GPS) corrected inertial navigation system (INS) seems to be advantageous to camera-based pose estimation algorithms for outdoor navigation. The GPS signals lead to a geographical location with an accuracy of a few meters or even up to some centimeters in a setup utilizing correction data. Performing the pose estimation with a camera system has several disadvantages...
Many robotic tasks rely on the accurate localization of moving objects within a given workspace. This information about the objects' poses and velocities are used for control, motion planning, navigation, interaction with the environment or verification. Often motion capture systems are used to obtain such a state estimate. However, these systems are often costly, limited in workspace size and not...
This paper presents a new robustification procedure for nonlinear least-squares optimisation problems. In particular, we focus on the robustness of view-graph SLAM against outlier correspondences in the images and outlier geometries in the graph. Our method utilises revised measurements model linearisation and decision making to detect and remove outliers during data fusion. We utilise innovations...
Although 3D reconstruction from a monocular video has been an active area of research for a long time, and the resulting models offer great realism and accuracy, strong conditions must be typically met when capturing the video to make this possible. This prevents general reconstruction of moving objects in dynamic, uncontrolled scenes. In this paper, we address this issue. We present a novel algorithm...
In this paper we suggest an improvement to a recent algorithm for estimating the pose and ego-motion of a camera which is constrained to planar motion at a constant height above the floor, with a constant tilt. Such motion is common in robotics applications where a camera is mounted onto a mobile platform and directed towards the floor. Due to the planar nature of the scene, images taken with such...
Considering the situation that performing indoor positioning system on mobile platform mostly relying on additional wireless signal and known environmental information, we propose an autonomous combination positioning method aimed at continuously estimating metric position and pose in an unknown scene. The vision-based SLAM algorithm extracts the image characteristics of the environment for producing...
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