Real-time photo-realistic 3D mapping for micro aerial vehicles

Heng,; Lee, Myeong Soo; Fraundorfer, Friedrich; Pollefeys,

doi:10.1109/iros.2011.6048818

Cited by 8 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aerial 3D Scanning and Mapping High-quality 3D reconstructions of very large scenes can be obtained using offline multi-view stereo algorithms [21] to process images acquired by drones [45]. Real-time mapping algorithms for drones have also been proposed, that take as input either RGBD [28,39,43,54] or RGB [62] images, and produce as output a 3D reconstruction of the scene. These methods are solving a reconstruction problem, and do not, themselves, generate drone trajectories.…”

Section: Related Workmentioning

confidence: 99%

Submodular Trajectory Optimization for Aerial 3D Scanning

Roberts

Shah

Dey

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

120

View full text Add to dashboard Cite

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a highfidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator.

show abstract

Section: Related Workmentioning

confidence: 99%

Submodular Trajectory Optimization for Aerial 3D Scanning

Roberts

Shah

Dey

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

120

View full text Add to dashboard Cite

show abstract

“…Shape features have also been studied, such as Hough Transform (Barrile et al, 2008), Local Contour Pattern (Landesa-Vazquez et al, 2010), or Local Binary Patterns (Liu et al, 2014). There are many intense efforts in computer vision (Heng et al, 2011, Crandall et al, 2011, Heng et al, 2011 focus on image-based 3D reconstruction at large-scale internet imagery. For example, image-based 3D point clouds and semantic texton forests are used to segment and recognize the highway assets (Golparvar-Fard et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Lidar-Incorporated Traffic Sign Detection From Video Log Images of Mobile Mapping System

Fan

Huang

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

show abstract

“…To date, very few works exist that combine inertial and RGBD measurements for navigation. In [1] and [2], rotational velocity measurements from the IMU are used for facilitating matching 3D point clouds from the RGBD images. The IMU measurements, however, are not fused with the RGBD observations in the estimation process.…”

Section: Introduction and Related Workmentioning

confidence: 99%

IMU-RGBD camera navigation using point and plane features

Guo

Roumeliotis

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-In this paper, we present a linear-complexity 3D inertial navigation algorithm using both point and plane features observed from an RGBD camera. In particular, we study the system's observability properties, and prove that: (i) When observing a single plane feature of known direction, the IMU gyroscope bias is observable. (ii) By observing a single point feature, as well as a single plane of known direction but not perpendicular to gravity, all degrees of freedom of the IMU-RGBD navigation system become observable, up to global translations. Next, based on the results of the observability analysis, we design a consistency-improved, observability-constrained (OC) extended Kalman filter (EKF)-based estimator for the IMU-RGBD camera navigation system. Finally, we experimentally validate the superiority of our proposed algorithm compared to alternative methods in urban scenes.

show abstract

Real-time photo-realistic 3D mapping for micro aerial vehicles

Cited by 8 publications

References 14 publications

Submodular Trajectory Optimization for Aerial 3D Scanning

Submodular Trajectory Optimization for Aerial 3D Scanning

Lidar-Incorporated Traffic Sign Detection From Video Log Images of Mobile Mapping System

IMU-RGBD camera navigation using point and plane features

Contact Info

Product

Resources

About