Monocular Vision-based Localization Research Articles

Monocular Vision-Based Localization and Pose Estimation with a Nudged Particle Filter and Ellipsoidal Confidence Tubes

This paper proposes a nudged particle filter for estimating the pose of a camera mounted on flying robots collecting a video sequence. The nudged particle filter leverages two image-to-pose and pose-to-image neural networks trained in an auto-encoder fashion with a dataset of pose-labeled images. Given an image, the retrieved camera pose using the image-to-pose network serves as a special particle to nudge the set of particles generated from the particle filter while the pose-to-image network serves to compute the likelihoods of each particle. We demonstrate that such a nudging scheme effectively mitigates low likelihood samplings during the particle propagation step. Ellipsoidal confidence tubes are constructed from the set of particles to provide a computationally efficient bound on localization error. When an ellipsoidal tube self-intersects, the probability volume of the intersection can be significantly shrunken using a novel Dempster–Shafer probability mass assignment algorithm. Starting from the intersection, a loop closure procedure is developed to move backward in time to shrink the volumes of the entire ellipsoidal tube. Experimental results using the Georgia Tech Miniature Autonomous Blimp platform are provided to demonstrate the feasibility and effectiveness of the proposed algorithms in providing localization and pose estimation based on monocular vision.

Outdoor SLAM Using Monocular Vision-Based Localization with LIDAR-Aided Mapping for service robot in Highway

This paper designed an intelligent service robot system in highway based on multi-sensor fusion. The mobile robot attempts to fuse the lidar information and monocular vision information to estimate the pose of itself and obtain an environmental map. It adapts a new SLAM method which combines lidar and vision information. Lidar is used to obtain the 2D occupancy grid map and the monocular vision SLAM algorithm uses the Extended Kalman Filter (EKF) to magnify the pose estimation. The 3-DOF pose provided by lidar is obtained through Cartographer algorithm and the monocular vision SLAM who offers the 6-DOF pose is realized with ORB-SLAM. The experimental results show that the system is effective in application as an intelligent service robot of highway.

Robust and Accurate Monocular Vision-Based Localization in Outdoor Environments of Real-World Robot Challenge

This paper describes our approach to perform robust monocular camera metric localization in the dynamic environments of Tsukuba Challenge 2016. We address two issues related to vision-based navigation. First, we improved the coverage by building a custom vocabulary out of the scene and improving upon place recognition routine which is key for global localization. Second, we established possibility of lifelong localization by using previous year’s map. Experimental results show that localization coverage was higher than 90% for six different data sets taken in different years, while localization average errors were under 0.2 m. Finally, the average of coverage for data sets tested with maps taken in different years was of 75%.

Monocular Vision-Based Localization Using ORB-SLAM with LIDAR-Aided Mapping in Real-World Robot Challenge

[abstFig src='/00280004/06.jpg' width='300' text='Monocular Visual Localization in Tsukuba Challenge 2015. Left: result of localization inside the map created by ORB-SLAM. Right: position tracking at starting point.' ] For the 2015 Tsukuba Challenge, we realized an implementation of vision-based localization based on ORB-SLAM. Our method combined mapping based on ORB-SLAM and Velodyne LIDAR SLAM, and utilized these maps in a localization process using only a monocular camera. We also apply sensor fusion method of odometer and ORB-SLAM from all maps. The combined method delivered better accuracy than the original ORB-SLAM, which suffered from scale ambiguities and map distance distortion. This paper reports on our experience when using ORB-SLAM for visual localization, and describes the difficulties encountered.

How to localize humanoids with a single camera?

In this paper, we propose a real-time vision-based localization approach for humanoid robots using a single camera as the only sensor. In order to obtain an accurate localization of the robot, we first build an accurate 3D map of the environment. In the map computation process, we use stereo visual SLAM techniques based on non-linear least squares optimization methods (bundle adjustment). Once we have computed a 3D reconstruction of the environment, which comprises of a set of camera poses (keyframes) and a list of 3D points, we learn the visibility of the 3D points by exploiting all the geometric relationships between the camera poses and 3D map points involved in the reconstruction. Finally, we use the prior 3D map and the learned visibility prediction for monocular vision-based localization. Our algorithm is very efficient, easy to implement and more robust and accurate than existing approaches. By means of visibility prediction we predict for a query pose only the highly visible 3D points, thus, speeding up tremendously the data association between 3D map points and perceived 2D features in the image. In this way, we can solve very efficiently the Perspective-n-Point (PnP) problem providing robust and fast vision-based localization. We demonstrate the robustness and accuracy of our approach by showing several vision-based localization experiments with the HRP-2 humanoid robot.

Communicating Vehicles in Convoy and Monocular Vision-based Localization

This article deals with mobile robotics in convoy. An autonomous vehicle has to follow the path of the vehicle ahead of it, but without having the vehicle ahead in its field of view. So, the task for the second vehicle is to follow the leader's path with a variable time delay. Moreover, relatively strong constraints are imposed in terms of speed of processing, precision of the localization and for communication. The vehicles of the convoy have to be localized in their environment without being detectable. So, the use of active sensors such as rangefinders, radars or laser sensors, is prohibited in the same way as GPS devices. The communication data rate between consecutive vehicles is low (10 kbits/s). The presented method allows in this context the replay of a path from input data based on 3D positions of some points supplied by the leader vehicle. This method combines data acquired by low cost sensors (odometer, gyroscope) and by a monocular camera through an Extended Kalman Filter (EKF). Simulation results as well as results of experiments conducted on real data demonstrate the effectiveness of the proposed approach.