Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Automated Visual Database Creation For A Ground Vehicle Simulator

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter

Terrain Classification from Body-mounted Cameras during Human Locomotion

Abstract—This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the fre-quency variations of the textured surface are analysed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility a hard surface, a soft surface and an unwalkable area- our proposed method outperforms existing methods by up to 16%, and also provides improved robustness. Index Terms—texture, classification, recursive filter, terrain classification I

On Semantic Segmentation and Path Planning for Autonomous Vehicles within Off-Road Environments

There are many challenges involved in creating a fully autonomous vehicle capable of safely navigating through off-road environments. In this work we focus on two of the most prominent such challenges, namely scene understanding and path planning. Scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we build on recent work in urban road-scene understanding, training a state of the art CNN architecture towards the task of classifying off-road scenes. We analyse the effects of transfer learning and training data set size on CNN performance, evaluating multiple configurations of the network at multiple points during the training cycle, investigating in depth how the training process is affected. We compare this CNN to a more traditional feature-driven approach with Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding. We then expand on this with the addition of multi-channel RGBD data, which we encode in multiple configurations for CNN input. We evaluate each of these configuration over our own off-road RGBD data set and compare performance to that of the network model trained using RGB data. Next, we investigate end-to-end navigation, whereby a machine learning algorithm optimises to predict the vehicle control inputs of a human driver. After evaluating such a technique in an off-road environment and identifying several limitations, we propose a new approach in which a CNN learns to predict vehicle path visually, combining a novel approach to automatic training data creation with state of the art CNN architecture to map a predicted route directly onto image pixels. We then evaluate this approach using our off-road data set, and demonstrate effectiveness surpassing existing end-to-end methods

A Visual AGV-Urban Car using Fuzzy Control

The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensor

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data

Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input. Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations. The best variations of Texture MCL improved accuracy by 19.1\% and 25.1\% when all particles and the top fifty particles respectively were used to calculate the robot\u27s estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot

Vegetation detection and terrain classification for autonomous navigation

Diese Arbeit beleuchtet sieben neuartige Ansätze aus zwei Bereichen der maschinellen Wahrnehmung: Erkennung von Vegetation und Klassifizierung von Gelände. Diese Elemente bilden den Kern eines jeden Steuerungssystems für effiziente, autonome Navigation im Außenbereich. Bezüglich der Vegetationserkennung, wird zuerst ein auf Indizierung basierender Ansatz beschrieben (1), der die reflektierenden und absorbierenden Eigenschaften von Pflanzen im Bezug auf sichtbares und nah-infrarotes Licht auswertet. Zweitens wird eine Fusionmethode von 2D/3D Merkmalen untersucht (2), die das menschliche System der Vegetationserkennung nachbildet. Zusätzlich wird ein integriertes System vorgeschlagen (3), welches die visuelle Wahrnehmung mit multi-spektralen Methoden ko mbiniert. Aufbauend auf detaillierten Studien zu Farb- und Textureigenschaften von Vegetation wird ein adaptiver selbstlernender Algorithmus eingeführt der robust und schnell Pflanzen(bewuchs) erkennt (4). Komplettiert wird die Vegetationserkennung durch einen Algorithmus zur Befahrbarkeitseinschätzung von Vegetation, der die Verformbarkeit von Pflanzen erkennt. Je leichter sich Pflanzen bewegen lassen, umso größer ist ihre Befahrbarkeit. Bezüglich der Geländeklassifizierung wird eine struktur-basierte Methode vorgestellt (6), welche die 3D Strukturdaten einer Umgebung durch die statistische Analyse lokaler Punkte von LiDAR Daten unterstützt. Zuletzt wird eine auf Klassifizierung basierende Methode (7) beschrieben, die LiDAR und Kamera-Daten kombiniert, um eine 3D Szene zu rekonstruieren. Basierend auf den Vorteilen der vorgestellten Algorithmen im Bezug auf die maschinelle Wahrnehmung, hoffen wir, dass diese Arbeit als Ausgangspunkt für weitere Entwicklung en von zuverlässigen Erkennungsmethoden dient.This thesis introduces seven novel contributions for two perception tasks: vegetation detection and terrain classification, that are at the core of any control system for efficient autonomous navigation in outdoor environments. Regarding vegetation detection, we first describe a vegetation index-based method (1), which relies on the absorption and reflectance properties of vegetation to visual light and near-infrared light, respectively. Second, a 2D/3D feature fusion (2), which imitates the human visual system in vegetation interpretation, is investigated. Alternatively, an integrated vision system (3) is proposed to realise our greedy ambition in combining visual perception-based and multi-spectral methods by only using a unit device. A depth study on colour and texture features of vegetation has been carried out, which leads to a robust and fast vegetation detection through an adaptive learning algorithm (4). In addition, a double-check of passable vegetation detection (5) is realised, relying on the compressibility of vegetation. The lower degree of resistance vegetation has, the more traversable it is. Regarding terrain classification, we introduce a structure-based method (6) to capture the world scene by inferring its 3D structures through a local point statistic analysis on LiDAR data. Finally, a classification-based method (7), which combines the LiDAR data and visual information to reconstruct 3D scenes, is presented. Whereby, object representation is described more details, thus enabling an ability to classify more object types. Based on the success of the proposed perceptual inference methods in the environmental sensing tasks, we hope that this thesis will really serve as a key point for further development of highly reliable perceptual inference methods

Visibility in underwater robotics: Benchmarking and single image dehazing

Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image.Una de las dificultades más grandes de la robótica autónoma submarina es lidiar con la falta de visibilidad en imágenes submarinas. La transmisión de la luz en el agua degrada las imágenes dificultando el reconocimiento de objetos y en consecuencia la intervención. Ésta tesis se centra en el análisis del impacto de la degradación de las imágenes submarinas en algoritmos de visión a través de benchmarking, desarrollando un entorno de trabajo en la nube que permite analizar los resultados bajo diferentes condiciones. Teniendo en cuenta los resultados obtenidos con este entorno, se proponen métodos basados en técnicas de aprendizaje profundo para mitigar el impacto de la degradación de las imágenes en tiempo real introduciendo un paso previo que permita recuperar los colores originales