scholarly journals Accurate and Robust Monocular SLAM with Omnidirectional Cameras

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4494 ◽  
Author(s):  
Liu ◽  
Guo ◽  
Feng ◽  
Yang
Keyword(s):  
Indoor Environment ◽  
Autonomous Driving ◽  
Field Of View ◽  
Wide Angle ◽  
Camera Model ◽  
Projection Function ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Catadioptric Systems ◽  
Real World Datasets

Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model. We open source in https://github.com/lsyads/fisheye-ORB-SLAM.

scholarly journals Large-Scale Outdoor SLAM Based on 2D Lidar

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 613 ◽  
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Meiping Wu
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Light Weight ◽  
Scan Matching ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Outdoor Environments ◽  
Real World Datasets

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.

scholarly journals Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1981
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Hanzhang Xue ◽  
Zhenping Sun ◽  
Kai Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Satellite System ◽  
Factor Graph ◽  
Mapping System ◽  
Global Navigation ◽  
Real World Datasets ◽  
Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

scholarly journals Real-Time Expanded Field-of-View for Minimally Invasive Surgery Using Multi-Camera Visual Simultaneous Localization and Mapping

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2106
Author(s):  
Ahmed Afifi ◽  
Chisato Takada ◽  
Yuichiro Yoshimura ◽  
Toshiya Nakaguchi
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Real Time ◽  
Invasive Surgery ◽  
Simultaneous Localization And Mapping ◽  
Field Of View ◽  
Time Dynamic ◽  
Matrix Estimation ◽  
Localization And Mapping

Minimally invasive surgery is widely used because of its tremendous benefits to the patient. However, there are some challenges that surgeons face in this type of surgery, the most important of which is the narrow field of view. Therefore, we propose an approach to expand the field of view for minimally invasive surgery to enhance surgeons’ experience. It combines multiple views in real-time to produce a dynamic expanded view. The proposed approach extends the monocular Oriented features from an accelerated segment test and Rotated Binary robust independent elementary features—Simultaneous Localization And Mapping (ORB-SLAM) to work with a multi-camera setup. The ORB-SLAM’s three parallel threads, namely tracking, mapping and loop closing, are performed for each camera and new threads are added to calculate the relative cameras’ pose and to construct the expanded view. A new algorithm for estimating the optimal inter-camera correspondence matrix from a set of corresponding 3D map points is presented. This optimal transformation is then used to produce the final view. The proposed approach was evaluated using both human models and in vivo data. The evaluation results of the proposed correspondence matrix estimation algorithm prove its ability to reduce the error and to produce an accurate transformation. The results also show that when other approaches fail, the proposed approach can produce an expanded view. In this work, a real-time dynamic field-of-view expansion approach that can work in all situations regardless of images’ overlap is proposed. It outperforms the previous approaches and can also work at 21 fps.

Wide-angle vision for road views

2013 ◽  
Vol 21 (1) ◽  
pp. 1-22 ◽  
Author(s):  
F. Huang ◽  
K. Fehrs ◽  
G. Hartmann ◽  
R. Klette
Keyword(s):  
Road Safety ◽  
3D Modelling ◽  
Field Of View ◽  
Wide Field ◽  
Wide Angle ◽  
Wide Field Of View ◽  
Imaging Sensors

AbstractThe field-of-view of a wide-angle image is greater than (say) 90 degrees, and so contains more information than available in a standard image. A wide field-of-view is more advantageous than standard input for understanding the geometry of 3D scenes, and for estimating the poses of panoramic sensors within such scenes. Thus, wide-angle imaging sensors and methodologies are commonly used in various road-safety, street surveillance, street virtual touring, or street 3D modelling applications. The paper reviews related wide-angle vision technologies by focusing on mathematical issues rather than on hardware.

Design of Mechaless LiDAR Optical System With Large FOV Using Liquid Lens and Fisheye Lens

2018 ◽  
Author(s):  
Hyun Choi ◽  
Wan-Chin Kim
Keyword(s):  
Low Cost ◽  
Autonomous Driving ◽  
Voltage Regulation ◽  
Field Of View ◽  
Fisheye Lens ◽  
Liquid Lens ◽  
Fine Spatial Resolution ◽  
Flash Lidar ◽  
Optical Phased Array ◽  
Measurable Distance

Mechaless LiDAR technology, which does not have a mechanical drive part, has been actively studied in order to increase the reliability of the LiDAR device at low cost and drive environment in order to more actively apply LiDAR technology to autonomous driving. Mechaless LiDAR technology, which has been mainly studied recently, includes 3D Flash LiDAR technology, MEMS mirror utilization method, and OPA (Optical Phased Array). However, these methods have not been developed rapidly as a key technology for achieving autonomous driving due to low stability of driving environment or remarkably low measurable distance and FOV (field of view) compared with mechanical LiDAR. In this study, we investigated the improvement of FOV by using a flux-deflecting liquid lens and a fisheye lens that can achieve fine spatial resolution through continuous voltage regulation. Based on the initial design results, it was examined that the FOV can be secured to 80 ° or more by utilizing a relatively simple fisheye lens composed of only spherical lenses.

scholarly journals Assessment of Stereoscopic Multi-resolution Images for a Virtual Reality System

2010 ◽  
Vol 9 (2) ◽  
pp. 31-37 ◽  
Author(s):  
Masahiko Ogawa ◽  
Kazunori Shidoji ◽  
Yuji Matsuki
Keyword(s):  
High Resolution ◽  
Field Of View ◽  
Monitor System ◽  
Wide Angle ◽  
Camera System ◽  
Technical Limitation ◽  
Resolution Image ◽  
The Gaze ◽  
High Resolution Image ◽  
Perceptual Characteristics

A camera and monitor system that projects actual real-world images has yet to be developed due to the technical limitation that the existing cameras cannot simultaneously acquire high-resolution and wide-angle images. In this research, we try to resolve this issue by superimposing images; a method which is effective because the entire wide-angle image does not necessarily need to be of high resolution because of perceptual characteristics of the human visual system. First, we examined the minimum resolution required for the field of view, which indicated that a triple-resolution image where positions more than 20 and 40 deg from the center of the visual field were decreased to 25% and approximately 11% of the resolution of the gaze point, respectively, was perceived as similar to a completely high-resolution image. Next, we investigated whether the participants could distinguish between the original completely high-resolution image and processed images, which included triple-resolution, dual-resolution, and low-resolution images. Our results suggested that the participants could not differentiate between the triple-resolution image and the original image. Finally, we developed a stereoscopic camera system based on our results

scholarly journals Profiling NVIDIA Jetson Embedded GPU Devices for Autonomous Machines

2020 ◽  
Author(s):  
Yazhou Li ◽  
Yahong Rosa Zheng
Keyword(s):  
Effective Means ◽  
Field Testing ◽  
Operating Mode ◽  
Autonomous Driving ◽  
Robot Operating System ◽  
Race Car ◽  
Localization And Mapping ◽  
Autonomous Machines ◽  
Particle Filter Algorithm ◽  
Embedded Gpu

This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.

scholarly journals On-chip monolithic wide-angle field-of-view metalens based on quadratic phase profile

AIP Advances ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 115213
Author(s):  
Cong Chen ◽  
Panpan Chen ◽  
Jianxin Xi ◽  
Wanxia Huang ◽  
Kuanguo Li ◽  
...  
Keyword(s):  
Field Of View ◽  
Wide Angle ◽  
Quadratic Phase ◽  
Phase Profile ◽  
On Chip

scholarly journals A Novel Feature-Based Approach for Indoor Monocular SLAM

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 305 ◽  
Author(s):  
Seyyed Hoseini ◽  
Peyman Kabiri
Keyword(s):  
Camera Tracking ◽  
Translation Error ◽  
Natural Feature ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Challenging Tasks ◽  
Camera Pose ◽  
Quality Video ◽  
Feature Based ◽  
Chessboard Pattern

Camera tracking and the construction of a robust and accurate map in unknown environments are still challenging tasks in computer vision and robotic applications. Visual Simultaneous Localization and Mapping (SLAM) along with Augmented Reality (AR) are two important applications, and their performance is entirely dependent on the accuracy of the camera tracking routine. This paper presents a novel feature-based approach for the monocular SLAM problem using a hand-held camera in room-sized workspaces with a maximum scene depth of 4–5 m. In the core of the proposed method, there is a Particle Filter (PF) responsible for the estimation of extrinsic parameters of the camera. In addition, contrary to key-frame based methods, the proposed system tracks the camera frame by frame and constructs a robust and accurate map incrementally. Moreover, the proposed algorithm initially constructs a metric sparse map. To this end, a chessboard pattern with a known cell size has been placed in front of the camera for a few frames. This enables the algorithm to accurately compute the pose of the camera and therefore, the depth of the primary detected natural feature points are easily calculated. Afterwards, camera pose estimation for each new incoming frame is carried out in a framework that is merely working with a set of visible natural landmarks. Moreover, to recover the depth of the newly detected landmarks, a delayed approach based on linear triangulation is used. The proposed method is applied to a realworld VGA quality video (640 × 480 pixels) where the translation error of the camera pose is less than 2 cm on average and the orientation error is less than 3 degrees, which indicates the effectiveness and accuracy of the developed algorithm.

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