Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice. (ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.Comment: 15 pages, 13 figure

Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees

<p>Abstract</p> <p>Background</p> <p>The detailed interpretation of mass phenomena such as human escape panic or swarm behaviour in birds, fish and insects requires detailed analysis of the 3D movements of individual participants. Here, we describe the adaptation of a 3D stereoscopic imaging method to measure the positional coordinates of individual agents in densely packed clusters. The method was applied to study behavioural aspects of shimmering in Giant honeybees, a collective defence behaviour that deters predatory wasps by visual cues, whereby individual bees flip their abdomen upwards in a split second, producing Mexican wave-like patterns.</p> <p>Results</p> <p>Stereoscopic imaging provided non-invasive, automated, simultaneous, <it>in-situ </it>3D measurements of hundreds of bees on the nest surface regarding their thoracic position and orientation of the body length axis. <it>Segmentation </it>was the basis for the <it>stereo matching</it>, which defined correspondences of individual bees in pairs of stereo images. Stereo-matched "agent bees" were re-identified in subsequent frames by the <it>tracking </it>procedure and <it>triangulated </it>into real-world coordinates. These algorithms were required to calculate the three spatial motion components (dx: horizontal, dy: vertical and dz: towards and from the comb) of individual bees over time.</p> <p>Conclusions</p> <p>The method enables the assessment of the 3D positions of individual Giant honeybees, which is not possible with single-view cameras. The method can be applied to distinguish at the individual bee level active movements of the thoraces produced by abdominal flipping from passive motions generated by the moving bee curtain. The data provide evidence that the z-deflections of thoraces are potential cues for colony-intrinsic communication. The method helps to understand the phenomenon of collective decision-making through mechanoceptive synchronization and to associate shimmering with the principles of wave propagation. With further, minor modifications, the method could be used to study aspects of other mass phenomena that involve active and passive movements of individual agents in densely packed clusters.</p

How to Join a Wave: Decision-Making Processes in Shimmering Behavior of Giant Honeybees (Apis dorsata)

Shimmering is a collective defence behaviour in Giant honeybees (Apis dorsata) whereby individual bees flip their abdomen upwards, producing Mexican wave-like patterns on the nest surface. Bucket bridging has been used to explain the spread of information in a chain of members including three testable concepts: first, linearity assumes that individual “agent bees” that participate in the wave will be affected preferentially from the side of wave origin. The directed-trigger hypothesis addresses the coincidence of the individual property of trigger direction with the collective property of wave direction. Second, continuity describes the transfer of information without being stopped, delayed or re-routed. The active-neighbours hypothesis assumes coincidence between the direction of the majority of shimmering-active neighbours and the trigger direction of the agents. Third, the graduality hypothesis refers to the interaction between an agent and her active neighbours, assuming a proportional relationship in the strength of abdomen flipping of the agent and her previously active neighbours. Shimmering waves provoked by dummy wasps were recorded with high-resolution video cameras. Individual bees were identified by 3D-image analysis, and their strength of abdominal flipping was assessed by pixel-based luminance changes in sequential frames. For each agent, the directedness of wave propagation was based on wave direction, trigger direction, and the direction of the majority of shimmering-active neighbours. The data supported the bucket bridging hypothesis, but only for a small proportion of agents: linearity was confirmed for 2.5%, continuity for 11.3% and graduality for 0.4% of surface bees (but in 2.6% of those agents with high wave-strength levels). The complimentary part of 90% of surface bees did not conform to bucket bridging. This fuzziness is discussed in terms of self-organisation and evolutionary adaptedness in Giant honeybee colonies to respond to rapidly changing threats such as predatory wasps scanning in front of the nest

Detecção de caminho em tempo real para veículo autônomo utilizando visão passiva

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Os veículos que trafegam de forma autônoma necessitam identificar ao longo do seu caminho vários elementos tais como: a estrada a seguir, se há necessidade de desviar de obstáculos fixos ou móveis e até mesmo parar. Esta dissertação tem como objetivo desenvolver uma metodologia para controlar a trajetória de um pequeno veículo através do seu sistema de rádio controle e equipado com câmera, utilizando técnicas da Visão Computacional e Processamento Digital de Imagens. Pode-se observar que diversos fatores impactam o controle do veículo pelo fato de estar em constante movimento. As mudanças constantes na iluminação natural, no tipo e na qualidade do terreno e a cor do cenário a ser trafegado, levaram a utilizar uma técnica adaptativa em tempo real nos algoritmos de segmentação para identificar a estrada e manter o veículo no caminho a ser seguido. Um pequeno circuito de testes em campo foi construído, para avaliar a metodologia desenvolvida simulando algumas situações reais de funcionamento. Os resultados dos testes indicaram que a metodologia desenvolvida foi capaz de manter autonomamente o veículo no caminho simulado, com diferentes tipos de terreno e em condições de variabilidade na iluminação natural.Abstract : The autonomous vehicles need to identify several elements such as the road to travel, deviate from fixed or mobile obstacles and even stop. This thesis aims to develop a methodology to control the trajectory of a small vehicle by radio control and equipped with camera, using techniques of Computer Vision and Digital Image Processing. It can be observed that several factors impact the control of the vehicle by the fact of being in constant motion. The constant changes in natural lighting, the type and quality of the land and the pixels color of the scene being trafficked, led to use an adaptive technique in real time on segmentation algorithms to identify the road and keep the vehicle on the way forward. A small testing circuit was built to evaluate the developed methodology simulating some real situations of operation. The test results indicated that this methodology was able to autonomously maintain the vehicle in the road, with different types of terrain and variability in natural daylight conditions

Fusion de données multi-capteurs pour la construction incrémentale du modèle tridimensionnel texturé d'un environnement intérieur par un robot mobile

Ce travail traite la Modélisation 3D d'un environnement intérieur par un robot mobile. La principale contribution concerne la construction d'un modèle géométrique hétérogène combinant des amers plans texturés, des lignes 3D et des points d'intérêt. Pour cela, nous devons fusionner des données géométriques et photométriques. Ainsi, nous avons d'abord amélioré la stéréovision, en proposant une approche de la mise en correspondance stéréoscopique par coupure de graphe. Notre contribution réside dans la construction d'un graphe réduit qui a permis d'accélérer la méthode globale et d'obtenir de meilleurs résultats que les méthodes locales. Aussi, pour percevoir l'environnement, le robot est équipé d'un télémètre laser 3D et d'une caméra. Nous proposons une chaîne algorithmique permettant de construire une carte hétérogène, par l'algorithme de Cartographie et Localisation Simultanées (EKF-SLAM). Le placage de la texture sur les facettes planes a permis de solidifier l'association de données.This thesis examines the problem of 3D Modelling of indoor environment by a mobile robot. Our main contribution consists in constructing a heterogeneous geometrical model containing textured planar landmarks, 3D lines and interest points. For that, we must fuse geometrical and photometrical data. Hence, we began by improving the stereo vision algorithm, and proposed a new approach of stereo matching by graph cuts. The most significant contribution is the construction of a reduced graph that allows to accelerate the global method and to provide better results than the local methods. Also, to perceive the environment, the robot is equipped by a 3D laser scanner and by a camera. We proposed an algorithmic chain allowing to incrementally constructing a heterogeneous map, using the algorithm of Simultaneous Localization and Mapping based (EKF-SLAM). Mapping the texture on the planar landmarks makes more robust the phase of data association