Parallel Tracking And Mapping Research Articles

Camera-Based Articulation Angle Sensing for Heavy Goods Vehicles

Articulation angle sensing is an essential component of manoeuvrability and stability control systems for articulated heavy goods vehicles, particularly long combination vehicles. Existing solutions to this sensing task are limited by reliance on trailer modifications or information or by measurement accuracy, or both, restricting commercial adoption. In this paper we present a purely tractor-based sensor concept comprising a rear-facing camera and the parallel tracking and mapping (PTAM) image processing algorithm. The system requires no prior knowledge of or modifications to the trailer, is compatible with planar and non-planar trailer shapes, and with multiply-articulated vehicle combinations. The system is validated in full-scale vehicle tests on both a tractor semi-trailer combination and a truck and full-trailer combination, demonstrating robust performance in a number of conditions, including trailers with non-planar geometry and with minimal visual features. Average RMS measurement errors of 1.19, 1.03 and 1.53 degrees were demonstrated for the semi-trailer and full-trailer (drawbar and semi-trailer) respectively. This compares favourably with the state-of-the-art in the published literature. A number of improvements are proposed for future development based on the observations in this research.

A Distributed Approach for the Implementation of Geometric Reconstruction-Based Visual SLAM Systems

SUMMARYVisual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the Parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.

Feature Tracking and Synchronous Scene Generation with a Single Camera

This paper shows a method of tracking feature points to update camera pose and generating a synchronous map for AR (Augmented Reality) system. Firstly we select the ORB (Oriented FAST and Rotated BRIEF) [1] detection algorithm to detect the feature points which have depth information to be markers, and we use the LK (Lucas-Kanade) optical flow [2] algorithm to track four of them. Then we compute the rotation and translation of the moving camera by relationship matrix between 2D image coordinate and 3D world coordinate, and then we update the camera pose. Last we generate the map, and we draw some AR objects on it. If the feature points are missing, we can compute the same world coordinate as the one before missing to recover tracking by using new corresponding 2D/3D feature points and camera poses at that time. There are three novelties of this study: an improved ORB detection, which can obtain depth information, a rapid update of camera pose, and tracking recovery. Referring to the PTAM (Parallel Tracking and Mapping) [3], we also divide the process into two parallel sub-processes: Detecting and Tracking (including recovery when necessary) the feature points and updating the camera pose is one thread. Generating the map and drawing some objects is another thread. This parallel method can save time for the AR system and make the process work in real-time.

Study on the ORB algorithm in the application of Monocular SLAM

In order to reduce the accumulative errors in our monocular SLAM (simultaneous localization and mapping), the loop closing (detection + correction) method based on PTAM (Parallel Tracking and Mapping) is applied. Here natural environment features are necessary to be extracted efficiently, so the ORB (Oriented FAST and Rotated BRIEF (Binary Robust Independent Elementary Features)) algorithm is used for the feature extraction and matching. The experiment results show that there is strong feature recognition power in ORB so that it can realize environment recognition under the conditions of severe viewpoint change. Moreover, it is so fast to extract and match (without using multi-threading or GPU (Graphics Processing Unit) acceleration) that it can accurately track and map in real time. Its capability of fast and efficiency is verified with outdoor scene experiments.

Monocular Camera based Trajectory Tracking of 3‐DOF Helicopter

AbstractThe vision based flight control problems of unmanned micro aerial vehicles (MAVs) have attracted much attention in recent years. This paper presents a new solution to the trajectory tracking problem of a three‐degrees‐of‐freedom (3‐DOF) helicopter by utilizing only an onboard monocular camera without any artificial marker. First, the parallel tracking and mapping (PTAM) algorithm, which is a famous solution to the visual simultaneous localization and mapping (vSLAM) problem, is employed to estimate the attitude angles of the 3‐DOF helicopter. Then the calibration puzzle of the mapping between the onboard camera and helicopter coordinate systems is turned into an optimization problem. A robust cost function is applied to get the accurate estimate of the mapping. Finally, for the purpose of alleviating the influences of nonlinearity and coupling between channels, the feedback linearization (FL) and the linear quadratic regulation (LQR) techniques are employed to design the controller. Experimental results show that the proposed method can ensure that the helicopter hovers without drift and has good tracking performance.

Monocular SLAM with Conditionally Independent Split Mapping

The recovery of structure from motion in real time over extended areas demands methods that mitigate the effects of computational complexity and arithmetical inconsistency. In this paper, we develop SCISM, an algorithm based on relative frame bundle adjustment, which splits the recovered map of 3D landmarks and keyframes poses so that the camera can continue to grow and explore a local map in real time while, at the same time, a bulk map is optimized in the background. By temporarily excluding certain measurements, it ensures that both maps are consistent, and by using the relative frame representation, new results from the bulk process can update the local process without disturbance. The paper first shows how to apply this representation to the parallel tracking and mapping (PTAM) method, a real-time bundle adjuster, and compares results obtained using global and relative frames. It then explains the relative representation's use in SCISM and describes an implementation using PTAM. The paper provides evidence of the algorithm's real-time operation in outdoor scenes, and includes comparison with a more conventional submapping approach.

Unified framework for recognition, localization and mapping using wearable cameras

Monocular approaches to simultaneous localization and mapping (SLAM) have recently addressed with success the challenging problem of the fast computation of dense reconstructions from a single, moving camera. Thus, if these approaches initially relied on the detection of a reduced set of interest points to estimate the camera position and the map, they are currently able to reconstruct dense maps from a handheld camera while the camera coordinates are simultaneously computed. However, these maps of 3-dimensional points usually remain meaningless, that is, with no memorable items and without providing a way of encoding spatial relationships between objects and paths. In humans and mobile robotics, landmarks play a key role in the internalization of a spatial representation of an environment. They are memorable cues that can serve to define a region of the space or the location of other objects. In a topological representation of the space, landmarks can be identified and located according to its structural, perceptive or semantic significance and distinctiveness. But on the other hand, landmarks may be difficult to be located in a metric representation of the space. Restricted to the domain of visual landmarks, this work describes an approach where the map resulting from a point-based, monocular SLAM is annotated with the semantic information provided by a set of distinguished landmarks. Both features are obtained from the image. Hence, they can be linked by associating to each landmark all those point-based features that are superimposed to the landmark in a given image (key-frame). Visual landmarks will be obtained by means of an object-based, bottom-up attention mechanism, which will extract from the image a set of proto-objects. These proto-objects could not be always associated with natural objects, but they will typically constitute significant parts of these scene objects and can be appropriately annotated with semantic information. Moreover, they will be affine covariant regions, that is, they will be invariant to affine transformation, being detected under different viewing conditions (view-point angle, rotation, scale, etc.). Monocular SLAM will be solved using the accurate parallel tracking and mapping (PTAM) framework by Klein and Murray in Proceedings of IEEE/ACM international symposium on mixed and augmented reality, 2007.

Wireless Vision-Based Stabilization of Indoor Microhelicopter

This paper presents wireless vision-based stabilization of an indoor microhelicopter via visual simultaneous localization and mapping. The so-called parallel tracking and mapping (PTAM) technique using a small single wireless camera on the helicopter is utilized to detect the position and attitude of the helicopter. We construct the measurement system that is able to calibrate the mapping between local coordinate system in the PTAM and world coordinate system and is able to realize noise detection and elimination. In addition, we design the guaranteed cost (stable) controller for the dynamics of the helicopter via a linear matrix inequality approach. Although path tracking control only via the small single wireless vision sensor is a quite difficult task, the control results demonstrate the utility of our approach.

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

The fusion of inertial and visual data is widely used to improve an object's pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object's absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor.