• 로봇학회논문지
• /
• 제7권3호
• /
• pp.171-181
• /
• 2012

Outdoor mobile robots are faced with various terrain types having different characteristics. To run safely and carry out the mission, mobile robot should recognize terrain types, physical and geometric characteristics and so on. It is essential to control appropriate motion for each terrain characteristics. One way to determine the terrain types is to use non-contact sensor data such as vision and laser sensor. Another way is to use contact sensor data such as slope of body, vibration and current of motor that are reaction data from the ground to the tire. In this paper, we presented experimental results on terrain classification using contact sensor data. We made a mobile robot for collecting contact sensor data and collected data from four terrains we chose for experimental terrains. Through analysis of the collecting data, we suggested a new method of terrain feature extraction considering physical characteristics and confirmed that the proposed method can classify the four terrains that we chose for experimental terrains. We can also be confirmed that terrain feature extraction method using Fast Fourier Transform (FFT) typically used in previous studies and the proposed method have similar classification performance through back propagation learning algorithm. However, both methods differ in the amount of data including terrain feature information. So we defined an index determined by the amount of terrain feature information and classification error rate. And the index can evaluate classification efficiency. We compared the results of each method through the index. The comparison showed that our method is more efficient than the existing method.

• 한국해양공학회지
• /
• 제8권2호
• /
• pp.64-79
• /
• 1994

In this study, terrain classification, which was done by using the quantitative classification parameters and suitable interpolation method was applied to improve the accuracy of digital elevation models, and to increase its practical use of aerial photogrammetry. A terrain area was classified into three groups using the quantitative classification parameters to the ratio of horizontal, inclined area, magnitude of harmonic vectors, deviation of vector, the number of breakline and proposed the suitable interpolation. Also, the accuracy of digital elevation models was improved in case of large grid intervals by applying combined interpolation suitable for each terrain group. As a result of this study, I have an algorithm to perform the classification of the topography in the area of interest objectively and decided optimal data interpolation scheme for given topography.

• 한국측량학회지
• /
• 제8권2호
• /
• pp.53-61
• /
• 1990

본 연구에서는 지형의 정량적 분석변수를 이용하여 지형을 분류하고, 지형에 따라 적절한 보간법을 적용하므로써 수치지형모형의 정확도 향상과 효용성을 높이는데 그 목적이 있다. 지형해석에 있어서 정량적 분류 변수를 이용하여 대상지역을 4개의 군집으로 분류하여 지형에 따른 경제적인 보간법을 적용하였으며 격자간격이 클 경우 각 지형군별로 보간법을 조합시킨 조합보간법을 적용하므로써 정확도를 향상시킬 수 있었다.

• 제어로봇시스템학회논문지
• /
• 제15권4호
• /
• pp.372-378
• /
• 2009

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

• 전자공학회논문지SC
• /
• 제45권6호
• /
• pp.55-59
• /
• 2008

야외 환경에서 무인차량의 자율주행에 있어서 효과적인 기동제어를 위해서는 장애물 탐지나 지형의 기하학적인 형상 정보외에 탐지된 장애물 및 지형 표면에 대한 재질 유형의 인식 및 분류 또한 중요한 요소이다. 영상 기반의 지표면 분류 알고리듬은 입력 영상에 대한 전처리, 특징추출, 분류 및 후처리의 절차로 수행된다. 본 논문에서는 컬러 CCD 카메라로부터 획득된 야외 지형영상에 대해 색상 및 질감 정보를 이용한 지형분류 기법을 제시한다. 전처리 단계에서 색공간 변환을 수행하고, 색상과 질감 정보를 이용하기 위해 웨이블릿 변환 특징을 사용하였으며, 분류기로서는 SVM(support vector machine)을 적용하였다. 야외 환경에서 획득된 실영상에 대한 실험을 통하여 제시된 알고리듬의 분류 성능을 평가하였으며, 제시된 알고리듬에 의한 효과적인 야지 지형분류의 가능성을 확인하였다.

• 로봇학회논문지
• /
• 제5권4호
• /
• pp.339-348
• /
• 2010

One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.

• 한국군사과학기술학회지
• /
• 제13권4호
• /
• pp.683-688
• /
• 2010

The ability to navigate autonomously in off-road terrain is the most critical technology needed for Unmanned Ground Vehicles(UGV). In this paper, we present a method for vision-based terrain cover classification using DCT features. To classify the terrain, we acquire image from a CCD sensor, then the image is divided into fixed size of blocks. And each block transformed into DCT image then extracts features which reflect frequency band characteristics. Neural network classifier is used to classify the features. The proposed method is validated and verified through many experiments and we compare it with wavelet feature based method. The results show that the proposed method is more efficiently classify the terrain-cover than wavelet feature based one.

• 한국도로학회논문집
• /
• 제13권4호
• /
• pp.221-229
• /
• 2011

도로 설계는 자연 지형에 순응하도록 선형을 결정함으로써 경제적이며 환경적인 피해가 최소화되는 도로 건설이 이루어지도록 할 필요가 있다. 현 도로설계기준은 지형을 평지와 산지로만 구분하고 있으나 국토의 25.8%가 구릉지이며 미국이나 호주 등 선진국의 경우도 지형을 평지, 구릉지, 산지로 세분화하여 자연 지형에 최대한 부합되는 설계를 유도하고 있음을 감안 시 구릉지를 포함한 세분화된 기준이 필요하다. 본 연구는 원지반의 기복량을 지표로 세 가지 독립된 지형간의 구분 기준을 정량적으로 제시하였다. 세분화된 지형 정의를 전제로 지형을 구분할 수 있는 방안에 대한 개념적 틀을 세우고 이를 도로설계 사례분석 등을 토대로 검토하였다. 연구 결론으로, 평지는 설계 단위구간(1km) 내 지반고 최고점과 최저점의 차이가 40m 미만, 구릉지는 40~60m 이내, 산지는 60m를 초과하는 것으로 제안하였다.

• 한국측량학회지
• /
• 제7권2호
• /
• pp.53-61
• /
• 1989

본 연구에서는 규칙격자형태의 자료에서 수치지형모델의 보간법이 적절히 적용될 수 있는 지형의 정량적분류 변수를 제시하고, 이 변수를 사용하여 군집분석함으로써 객관적인 지형분류를 확립하며, 또한 지형의 분류변수에 의한 판별 분석으로 임의의 지형이 분류된 지형군 중 어느 지형에 속하는지 판별하여 지형특성에 적절한 보간법을 적용하는 데에 그 목적이 있다. 본 연구에서 지형분류는 수평면적에 대한 경사면적의 비와 조화벡터량의 두 변수를 사용하여 대상지형을 3개의 지형군으로 나눌 수 있었다. 3개의 지형군은 판별함수식으로 구별할 수 있었으며, 임의 지형에 대해 수평면적에 대한 경사면적의 비와 조화벡터량을 구하고, 판별함수를 이용하여 지형특성에 맞는 보간법을 적용할 수 있었다.

• 한국군사과학기술학회지
• /
• 제13권5호
• /
• pp.894-902
• /
• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.