Duong Nguyen

Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system

Fully autonomous navigation has been widely investigated for several decade of years; however, a safe and reliable navigation is still a daunting challenge in terrains containing vegetation. To improve the mobility capability of recent autonomous navigation systems, an additional vegetation detection function has been proposed. Since many proposals of generating vegetation classifier as well as suggestions of using different sensors to implement the function exist, a structured overview is required for vegetation detection in the context of outdoor navigation. Therefore, this paper studies and compares the accuracy and efficiency of existing vegetation detection approaches in a structured way. Furthermore, a new vision system set-up which combines CMOS sensor and Photo Mixer Device sensor with a near-infrared lighting system is also introduced to simultaneously provide depth, near-infrared and color images at high frame rate. Those near-infrared and color information are then used to compute vegetation index or train vegetation classifier to completely realize a real-time robust vegetation detection system. In this paper, a modification of the normalized difference vegetation index is devised, which is then defined as the new standard form of vegetation index for such vision system integrated with an additional lighting system. Finally, we will show the out-performance of the proposed approach in comparison with more conventional ones.

A novel approach for a double-check of passable vegetation detection in autonomous ground vehicles

The paper introduces an active way to detect vegetation which is at front of the vehicle in order to give a better decision-making in navigation. Blowing devices are to be used for creating strong wind to effect vegetation. Motion compensation and motion detection techniques are applied to detect foreground objects which are presumably judged as vegetation. The approach enables a double-check process for vegetation detection which was done by a multi-spectral approach, but more emphasizing on the purpose of passable vegetation detection. In all real world experiments we carried out, our approach yields a detection accuracy of over 98%. We furthermore illustrate how the active way can improve the autonomous navigation capabilities of autonomous ground vehicles.

Terrain classification based on structure for autonomous navigation in complex environments

One of the main challenges for autonomous navigation in cluttered outdoor environments is to determine which obstacles can be driven over and which need to be avoided. Especially in off-road driving, the aim is not only to recognize the lethal obstacles on the vehicles way at all costs, but also to predict the scene category thereby giving a better decision-making framework for vehicle navigation. This paper studies terrain classification based on structure relying on sparse 3-D data from LADAR mobility sensors. While most of recent methods for LADAR processing are purely found on the local point density and spatial distribution of the 3-D point cloud directly. We, on the other hand, introduce a new approach to analyze the point cloud by considering local properties and distance variation of pixels inside edgeless areas. First of all, the edgeless areas are extracted from segmenting the 3-D point cloud into homogeneous regions by Graph-Cut technique. Secondly, the neighbor distance variation inside edgeless areas (NDVIE) features are obtained by calculating the euclidean distance of neighbor distance variation inside each region. Through extensive experiments, we demonstrate that this feature has properties complementary to the conditional local point statistics features traditionally used for point cloud analysis, and show significant improvement in classification performance for tasks relevant to outdoor navigation.

Multiple templates auto exposure control based on luminance histogram for onboard camera

In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.

Auto White Balance Using the Coincidence of Chromaticity Histograms

This paper presents a simple and robust auto white balance algorithm using the coincidence of chromaticity histograms. After analyzing the relationship between the coincidence of the color histogram and color constancy, the overlap area of chromaticity histograms that keep chromaticity of color images but not the effect of luminance existing in the color histogram is employed to estimate the correct illuminant in scenes. When the overlap reaches the maximum, correspondingly the respective gain coefficients of color channels can be derived to achieve the white balance of the camera. Through numerous experiments and evaluations based on the processing of real world images, the proposed coincidence of chromaticity histograms algorithm achieves the outstanding performance comparing to other algorithms. Furthermore, the simplicity, easy implementation and robustness to the luminance make it flexibly apply to the vision system of the autonomous robot running outdoor.

Cooperation in heterogeneous groups of autonomous robots

By increasing complexity of tasks, autonomous outdoor robots emerge from single beings into cooperating groups. This paper first introduces the robots developed at the University of Siegen and then discusses the architecture and communication structure that is able to glue these into a group working on a common task while exploiting their different abilities. The paper describes a new software system that universally connects quite different robots into one group. Based on VSAL middle ware and ROS the CAPTAIN architecture implements complex missions via hierarchic state machines and an appropriate control scheme. This system is verified experimentally by using it in several different groups of robots.

Fitting plane algorithm-based depth correction for Tyzx DeepSea stereoscopic imaging

The work presented in this paper deals with the poor performance of depth image generation given by Tyzx stereo vision system under different lighting conditions in both indoor and outdoor environments. For that aim, we introduce a fitting plane algorithm to correct distance information as well as fulfill the missing points in the original depth. First, the color image is over-segmented into many small homogeneous regions of interest. Those small regions can be approximately considered as planar surfaces which form the 3-d scene. While 3D points inside each small region should found a plane, this insight is then used to enhance the depth image. Assuming that the environment is made up of a number of small planes, we certainly make no explicit assumptions about the structure of the scene; this enables the algorithm to cope up with many different scenes even with significant non-vertical structure. The algorithm has been confirmed to be easily implemented and robust throughout many experiments in different lighting conditions and different scenarios in both indoor and outdoor environments. Concretely, the proposed approach enables a 3-d reconstruction capability using Tyzx DeepSea G3 vision system which is infeasible from the raw depth data. Moreover, the proposed algorithm improves more than 48% of 3-d reconstruction accuracy compared with the original result given by the stereo vision system over testing 611 scenes under real-time constraint.

A flexible auto white balance based on histogram overlap

Auto white-balance plays a very important role in computer vision, and also is a prerequisite of color processing algorithms. For keeping the color constancy in the real-time outdoor environment, a simple and flexible auto white balance algorithm based on the color histogram overlap of the image is presented in this paper. After looking at a numerous images under different illuminance, an essential characteristic of the white-balance, the color histogram coincidence, is generalized as the basic criterion. Furthermore the overlap area of the color histogram directly reflects this coincidence, namely, when the overlap area of the color histogram reaches the maximum, the respective gain coefficients of color channels can be derived to achieve the white-balance of the camera. Through the subjective and objective evaluations based on the processing of real world images, the proposed histogram overlap algorithm can not only flexibly implement the auto white-balance of the camera but also achieve the outstanding performance in the real-time outdoor condition.