A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

Kang, Yeonsik; Roh, Chi-Won; Suh, Soong‐Hyuck; Song, Bang‐Sup

doi:10.1109/tie.2012.2185013

Cited by 94 publications

(26 citation statements)

References 20 publications

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“…Particularly at high speeds, state estimation filters (e.g. the extended Kalman filter commonly used in navigation robots [30], [31]) may help to reduce following errors by predicting future positions based on current system states.…”

Section: Experimental Methods and Resultsmentioning

confidence: 99%

Adaptive Kinematic Control of a Robotic Venipuncture Device Based on Stereo Vision, Ultrasound, and Force Guidance

Balter

Chen

Maguire³

et al. 2017

IEEE Trans. Ind. Electron.

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Robotic systems have slowly entered the realm of modern medicine; however, outside the operating room, medical robotics has yet to be translated to more routine interventions such as blood sampling or intravenous fluid delivery. In this paper, we present a medical robot that safely and rapidly cannulates peripheral blood vessels—a procedure commonly known as venipuncture. The device uses near-infrared and ultrasound imaging to scan and select suitable injection sites, and a 9-DOF robot to insert the needle into the center of the vessel based on image and force guidance. We first present the system design and visual servoing scheme of the latest generation robot, and then evaluate the performance of the device through workspace simulations and free-space positioning tests. Finally, we perform a series of motion tracking experiments using stereo vision, ultrasound, and force sensing to guide the position and orientation of the needle tip. Positioning experiments indicate sub-millimeter accuracy and repeatability over the operating workspace of the system, while tracking studies demonstrate real-time needle servoing in response to moving targets. Lastly, robotic phantom cannulations demonstrate the use of multiple system states to confirm that the needle has reached the center of the vessel.

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Section: Experimental Methods and Resultsmentioning

confidence: 99%

Adaptive Kinematic Control of a Robotic Venipuncture Device Based on Stereo Vision, Ultrasound, and Force Guidance

Balter

Chen

Maguire³

et al. 2017

IEEE Trans. Ind. Electron.

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“…Another method proposed by Kang [7] for road boundary detection used multiple kalman filters to integrate the sensor measurements which uses a 2-D lidar sensor. The lidar sensor is mounted with its scanning plane tilted to extract a uniform geometrical feature.…”

Section: Related Workmentioning

confidence: 99%

Detection of road boundaries and obstacles using LIDAR

Özkan¹,

Sayar

Arar

et al. 2014

2014 6th Computer Science and Electronic Engineering Conference (CEEC)

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This paper proposes a novel approach to detecting road boundaries and obstacles for robust urban navigation of unmanned ground vehicles (UGVs). In this method, a 2-D Light Detection and Ranging (LIDAR) sensor mounted at a given pitch angle is used to extract the road surface. The information obtained about the road is evaluated by filters to detect whether the area scanned is a road or an obstacle. The proposed method is implemented through simulation and a real-life test vehicle called AKAY01.

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“…For collision warning, this approach adopts the probability density function (pdf) of a collision position predicted by the current target state [14][15][16][17][18]. It has advantages over the TTC based schemes because it can provide mathematically rigorous way to define collision probability as a threat measure.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture approach to decision making for automotive collision warning systems

Han

Sang

Whang

et al. 2015

Int. J. Control Autom. Syst.

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This paper proposes a practical probabilistic approach to collision decision making which is necessary for advanced automotive collision warning system (CWS) using FMCW radar. Most decision making algorithms assess the probable collisions based on the predicted collision position which is usually expressed as a nonlinear function of threat vehicle's position and velocity provided by FMCW radar. Since the predicted collision position has highly nonlinear statistics in general, it is one of main obstacles to improving the reliability of the collision probability computation and to developing real-time decision making algorithms. This motivates us to devise a Gaussian mixture method for collision probability calculation with the help of linear recursive time-to-collision (TTC) estimation. The suggested TTC estimator provides an accurate TTC estimate with small estimation error variance hence it enables us to approximate the probability density function of the predicted collision position as the weighted sum of just a few Gaussian distributions. Therefore, our approach could drastically reduce the inherent nonlinearity of collision decision making problem and computational complexity in collision probability calculation. Through the simulations for the typical engagement scenarios between the host and threat vehicles, the performance and effectiveness of the proposed algorithm is compared to those of the existing ones which require heavy computational burden.

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A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

Cited by 94 publications

References 20 publications

Adaptive Kinematic Control of a Robotic Venipuncture Device Based on Stereo Vision, Ultrasound, and Force Guidance

Adaptive Kinematic Control of a Robotic Venipuncture Device Based on Stereo Vision, Ultrasound, and Force Guidance

Detection of road boundaries and obstacles using LIDAR

Gaussian mixture approach to decision making for automotive collision warning systems

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