17,802 research outputs found
Precise localization for aerial inspection using augmented reality markers
The final publication is available at link.springer.comThis chapter is devoted to explaining a method for precise localization using augmented reality markers. This method can achieve precision of less of 5 mm in position at a distance of 0.7 m, using a visual mark of 17 mm × 17 mm, and it can be used by controller when the aerial robot is doing a manipulation task. The localization method is based on optimizing the alignment of deformable contours from textureless images working from the raw vertexes of the observed contour. The algorithm optimizes the alignment of the XOR area computed by means of computer graphics clipping techniques. The method can run at 25 frames per second.Peer ReviewedPostprint (author's final draft
Radar-on-Lidar: metric radar localization on prior lidar maps
Radar and lidar, provided by two different range sensors, each has pros and
cons of various perception tasks on mobile robots or autonomous driving. In
this paper, a Monte Carlo system is used to localize the robot with a rotating
radar sensor on 2D lidar maps. We first train a conditional generative
adversarial network to transfer raw radar data to lidar data, and achieve
reliable radar points from generator. Then an efficient radar odometry is
included in the Monte Carlo system. Combining the initial guess from odometry,
a measurement model is proposed to match the radar data and prior lidar maps
for final 2D positioning. We demonstrate the effectiveness of the proposed
localization framework on the public multi-session dataset. The experimental
results show that our system can achieve high accuracy for long-term
localization in outdoor scenes
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