Robust outdoor stereo vision SLAM for heavy machine rotation sensing

Lin, Liheng; Lawrence, P.D.; Hall, Robert A.

doi:10.1007/s00138-011-0380-6

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…A stereo camera was installed on the boom of a mining shovel to estimate pose of haul trucks in [6], yet the shovel's own pose was unknown. In [7] the shovel's swing rotation was recovered using stereo vision SLAM, yet the pose of its buckets was not estimated. This type of methods can be infrastructure independent if with SLAM, yet some problems (sensitivity to lighting changes or texture-less regions) remain to be resolved for more robust applications.…”

Section: Previous Workmentioning

confidence: 99%

Vision-Based Articulated Machine Pose Estimation for Excavation Monitoring and Guidance

Feng¹,

Dong²,

Lundeen³

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The pose of an articulated machine includes the position and orientation of not only the machine base (e.g., tracks or wheels), but also each of its major articulated components (e.g., stick and bucket). The ability to automatically estimate this pose is a crucial component of technical innovations aimed at improving both safety and productivity in many construction tasks. A computer vision based solution using a network of cameras and markers is proposed in this research to enable such a capability for articulated machines. Firstly, a planar marker is magnetically mounted on the end-effector of interest. Another marker is fixed on the jobsite whose 3D pose is pre-surveyed in a project coordinate frame. Then a cluster of at least two cameras respectively observing and tracking the two markers simultaneously forms a camera-marker network and transfers the end-effector's pose into the desired project frame, based on a pre-calibration of the relative poses between each pair of cameras. Through extensive sets of uncertainty analyses and field experiments, this approach is shown to be able to achieve centimeter level depth tracking accuracy within up to 15 meters with only two ordinary cameras (1.1 megapixel each) and a few markers, providing a flexible and cost-efficient alternative to other commercial products that use infrastructure dependent sensors like GPS. A working prototype has been tested on several active construction sites with positive feedback from excavator operators confirming the solution's effectiveness.

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Section: Previous Workmentioning

confidence: 99%

Vision-Based Articulated Machine Pose Estimation for Excavation Monitoring and Guidance

Feng¹,

Dong²,

Lundeen³

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…It uses a prior vehicle state estimate to predict current feature locations and then compares this prediction to current observations to calculate an updated vehicle state. The state estimates delivered by the Kalman Filter utilizes any available information to minimize the mean of the squared error of the estimates with regard to the available information (Lin et al 2013). In Helmick et al (2004), a Kalman filter pose estimator has been implemented with VO system for autonomous rover in high slip environments.…”

Section: Vomentioning

confidence: 99%

Review of visual odometry: types, approaches, challenges, and applications

et al. 2016

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Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time. Vision-based odometry is a robust technique utilized for this purpose. It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle. This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges. VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors. Several areas for future research are also highlighted.

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“…Recently, a large amount of research has illustrated that MAVs can operate using standard stereo cameras [ 12 , 55 , 56 ]. The main purpose of these contributions is to show that an expensive sensor is not needed to create an automated MAV.…”

Section: Slammentioning

confidence: 99%

A Primer on Autonomous Aerial Vehicle Design

Coppejans

Myburgh

2015

Sensors

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There is a large amount of research currently being done on autonomous micro-aerial vehicles (MAV), such as quadrotor helicopters or quadcopters. The ability to create a working autonomous MAV depends mainly on integrating a simultaneous localization and mapping (SLAM) solution with the rest of the system. This paper provides an introduction for creating an autonomous MAV for enclosed environments, aimed at students and professionals alike. The standard autonomous system and MAV automation are discussed, while we focus on the core concepts of SLAM systems and trajectory planning algorithms. The advantages and disadvantages of using remote processing are evaluated, and recommendations are made regarding the viability of on-board processing. Recommendations are made regarding best practices to serve as a guideline for aspirant MAV designers.

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Robust outdoor stereo vision SLAM for heavy machine rotation sensing

Cited by 7 publications

References 16 publications

Vision-Based Articulated Machine Pose Estimation for Excavation Monitoring and Guidance

Vision-Based Articulated Machine Pose Estimation for Excavation Monitoring and Guidance

Review of visual odometry: types, approaches, challenges, and applications

A Primer on Autonomous Aerial Vehicle Design

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