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Closed Loop-based extrinsic calibration of multi-modal sensors
Sungdae Sim,Kiho Kwak,Jun Kim,Sang Hyun Joo 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
By increasing the requirement of reliable and accurate sensor information, the integration of multiple sensors has gained attention. Especially, the fusion of a LIDAR(Light Detection And Ranging) and a camera is one of the sensor combination broadly used because it provides the complementary and redundant information. Many existing calibration approaches consider the problem estimating the relative pose between each sensor pair such as a LIDAR and a camera. However, these approaches do not provide accurate solutions for multisensor configurations such as a LIDAR and cameras or LIDARs and cameras. In this paper, we propose a new extrinsic calibration algorithm using closed-loop constraints for multi-modal sensor configuration. The extrinsic calibration parameters are estimated by minimizing the distance between corresponding features projected onto the image plane. We conduct several experiments to evaluate the performance of our approach, such as comparison of the RMS distance of the ground truth and the projected points, and comparison between the independent sensor pair and our approach.
임의 주행 기반 3차원 라이다 좌표와 차량 차표의 외부 파라미터 캘리브레이션 기법
심성대(Sungdae Sim),민지홍(Jihong Min),김준(Jun Kim) 대한기계학회 2016 대한기계학회 춘추학술대회 Vol.2016 No.12
In many autonomous vehicles, 3D LADARs have become a very important sensor. In many case, we put it on someplace of vehicle such as the roof. To integrate sensor informations from time to time, we should know the sensor’s movement. In many applications, we know the movement from the vehicle’s movement via navigation systems such as GPS/IMU or vehicle odometers. When we know the rotations and translations between 3D LADAR and vehicle coordinates, the movement of the sensor can be obtain accurately via a simple transformation. We estimate the extrinsic parameters using iterative closest point algorithm and nonlinear optimization. Experimental results show the proposed algorithm works well.
심성대(Sungdae Sim),민지홍(Jihong Min),김병준(Byungjun Kim),김준모(Junmo Kim) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.11
Trajectory prediction is one of the important technology for autonomous vehicles and robots. Recently, many learning-based trajectory prediction algorithms are released. Most approaches tend to use recurrent neural networks such as LSTM. Our approach is trajectory prediction using attention mechanisms. Our experiments show the attention mechanism outperforms RNN.
A Fast Ground Segmentation Method for 3D Point Cloud
( Phuong Chu ),( Seoungjae Cho ),( Sungdae Sim ),( Kiho Kwak ),( Kyungeun Cho ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.3
In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.
A dynamic zone estimation method using cumulative voxels for autonomous driving
Lee, Seongjo,Cho, Seoungjae,Sim, Sungdae,Kwak, Kiho,Park, Yong Woon,Cho, Kyungeun SAGE Publications 2017 International Journal of Advanced Robotic Systems Vol.14 No.1
<P>Obstacle avoidance and available road identification technologies have been investigated for autonomous driving of an unmanned vehicle. In order to apply research results to autonomous driving in real environments, it is necessary to consider moving objects. This article proposes a preprocessing method to identify the dynamic zones where moving objects exist around an unmanned vehicle. This method accumulates three-dimensional points from a light detection and ranging sensor mounted on an unmanned vehicle in voxel space. Next, features are identified from the cumulative data at high speed, and zones with significant feature changes are estimated as zones where dynamic objects exist. The approach proposed in this article can identify dynamic zones even for a moving vehicle and processes data quickly using several features based on the geometry, height map and distribution of three-dimensional space data. The experiment for evaluating the performance of proposed approach was conducted using ground truth data on simulation and real environment data set.</P>
A Fast Ground Segmentation Method for 3D Point Cloud
Chu, Phuong,Cho, Seoungjae,Sim, Sungdae,Kwak, Kiho,Cho, Kyungeun Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.3
In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.