Giving robots the ability to move around autonomously in various real-world environments has long been a major challenge for Artificial Intelligence. New approaches to the design and control of autonomous robots have shown the value of drawing inspiration from the natural world. Animals navigate, perceive and interact with various uncontrolled environments with seemingly little effort. Flying insects, in particular, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments.Inspired by the miniature machine view of insects, this thesis contributes to the autonomous control of mobile robots through the application of insect-based visual cues and behaviours. The parsimonious, yet robust, solutions offered by insects are directly applicable to the computationally restrictive world of autonomous mobile robots. To this end, two main navigational domains are focussed on: corridor guidance and visual homing.Within a corridor environment, safe navigation is achieved through the application of simple and intuitive behaviours observed in insect, visual navigation. By observing and responding to observed apparent motions in a reactive, yet intelligent way, the robot is able to exhibit useful corridor guidance behaviours at modest expense. Through a combination of both simulation and real-world robot experiments, the feasibility of equipping a mobile robot with the ability to safely navigate in various environments, is demonstrated.It is further shown that the reactive nature of the robot can be augmented to incorporate a map building method that allows previously encountered corridors to be recognised, through the observation of landmarks en route. This allows for a more globally-directed navigational goal.Many animals, including insects such as bees and ants, successfully engage in visual homing. This is achieved through the association of ++ / visual landmarks with a specific location. In this way, the insect is able to 'home in' on a previously visited site by simply moving in such a way as to maximise the match between the currently observed environment and the memorised 'snapshot' of the panorama as seen from the goal. A mobile robot can exploit the very same strategy to simply and reliably return to a previously visited location.This thesis describes a system that allows a mobile robot to home successfully. Specifically, a simple, yet robust, homing scheme that relies only upon the observation of the bearings of visible landmarks, is proposed. It is also shown that this strategy can easily be extended to incorporate other visual cues which may improve overall performance.The homing algorithm described, allows a mobile robot to home incrementally by moving in such a way as to gradually reduce the discrepancy between the current view and the view obtained from the home position. Both simulation and mobile robot experiments are again used to demonstrate the feasibility of the approach.

This dissertation proposes autonomous robotic strategies for urban search and rescue (USAR) which are map-based semi-autonomous robot navigation and fully-autonomous robotic search, tracking, localization and mapping (STLAM) using a team of robots. Since the prerequisite for these solutions is accurate robot localization in the environment, this dissertation first presents a novel grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan and estimation of the robot pose, the proposed technique corrects the estimation error by matching the new scan to the globally defined grid map. To improve the accuracy of the correction, each grid cell of the map is represented by multiple normal distributions (NDs). The new scan to be matched to the map is also represented by NDs, which achieves the scan-to-map matching by the ND-to-ND matching. In the map-based semi-autonomous robot navigation strategy, a robot placed in an environment creates the map of the environment and sends it to the human operator at a distant location. The human operator then makes decisions based on the map and controls the robot via tele-operation. In case of communication loss, the robot semi-autonomously returns to the home position by inversely tracking its trajectory with additional optimal path planning. In the fully-autonomous robotic solution to USAR, multiple robots communicate one another while operating together as a team. The base station collects information from each robot and assigns tasks to the robots. Unlike the semi-autonomous strategy there is no control from the human operator. To further enhance the efficiency of their cooperation each member of the team specifically works on its own task. A series of numerical and experimental studies were conducted to demonstrate the applicability of the proposed solutions to USAR scenarios. The effectiveness of the scan-to-map matching with the multi-ND representation was confirmed by analyzing the error accumulation and by comparing with the single-ND representation. The applicability of the scan-to-map matching to the real SLAM problem was also verified in three different real environments. The results of the map-based semi-autonomous robot navigation showed the effectiveness of the approach as an immediately usable solution to USAR. The effectiveness of the proposed fully- autonomous solution was first confirmed by two real robots in a real environment. The cooperative performance of the strategy was further investigated using the developed platform- and hardware-in-the-loop simulator. The results showed significant potential as the future solution to USAR. / Ph. D.

O problema da localização e mapeamento simultâneos, conhecido como problema do SLAM, é um dos maiores desafios que a robótica móvel autônoma enfrenta atualmente. Esse problema surge devido à dificuldade que um robô apresenta ao navegar por um ambiente desconhecido, construindo um mapa das regiões por onde já passou ao mesmo tempo em que se localiza dentro dele. O acúmulo de erros gerados pela imprecisão dos sensores utilizados para estimar os estados de localização e mapeamento impede que sejam obtidos resultados confiáveis após períodos de navegação suficientemente longos. Algoritmos de SLAM procuram eliminar esses erros resolvendo ambos os problemas simultaneamente, utilizando as informações de uma etapa para aumentar a precisão dos resultados alcançados na outra e viceversa. Uma das maneiras de se alcançar isso se baseia no estabelecimento de marcos no ambiente que o robô pode utilizar como pontos de referência para se localizar conforme navega. Esse trabalho apresenta uma solução para o problema do SLAM que faz uso de um sensor de visão omnidirecional para estabelecer esses marcos. O uso de sistemas de visão permite a extração de marcos naturais ao ambiente que podem ser correspondidos de maneira robusta sob diferentes pontos de vista. A visão omnidirecional amplia o campo de visão do robô e com isso aumenta a quantidade de marcos observados a cada instante. Ao ser detectado o marco é adicionado ao mapa que robô possui do ambiente e, ao ser reconhecido, o robô pode utilizar essa informação para refinar suas estimativas de localização e mapeamento, eliminando os erros acumulados e conseguindo mantê-las precisas mesmo após longos períodos de navegação. Essa solução foi testada em situações reais de navegação, e os resultados mostram uma melhora significativa nos resultados alcançados em relação àqueles obtidos com a utilização direta das informações coletadas. / The problem of simultaneous localization and mapping, known as the problem of SLAM, is one of the greatest obstacles that the field of autonomous robotics faces nowadays. This problem is related to a robots ability to navigate through an unknown environment, constructing a map of the regions it has already visited at the same time as localizing itself on this map. The imprecision inherent to the sensors used to collect information generates errors that accumulate over time, not allowing for a precise estimation of localization and mapping when used directly. SLAM algorithms try to eliminate these errors by taking advantage of their mutual dependence and solving both problems simultaneously, using the results of one step to refine the estimatives of the other. One possible way to achieve this is the establishment of landmarks in the environment that the robot can use as points of reference to localize itself while it navigates. This work presents a solution to the problem of SLAM using an omnidirectional vision system to detect these landmarks. The choice of visual sensors allows for the extraction of natural landmarks and robust matching under different points of view, as the robot moves through the environment. The omnidirectional vision amplifies the field of vision of the robot, increasing the number of landmarks observed at each instant. The detected landmarks are added to the map, and when they are later recognized they generate information that the robot can use to refine its estimatives of localization and mapping, eliminating accumulated errors and keeping them precise even after long periods of navigation. This solution has been tested in real navigational situations and the results show a substantial improvement in the results compared to those obtained through the direct use of the information collected.

Modern applications of mobile robots require them to have the ability to safely and effectively navigate in human environments. New challenges arise when these robots must plan their motion in a human-aware fashion. Current methods addressing this problem have focused mainly on the activity forecasting aspect, aiming at improving predictions without considering the active nature of the interaction, i.e. the robot’s effect on the environment and consequent issues such as reciprocity. Furthermore, many methods rely on computationally expensive offline training of predictive models that may not be well suited to rapidly evolving dynamic environments. This thesis presents a novel approach for enabling autonomous robots to navigate socially in environments with humans. Following formulations of the inverse planning problem, agents reason about the intentions of other agents and make predictions about their future interactive motion. A technique is proposed to implement counterfactual reasoning over a parametrised set of light-weight reciprocal motion models, thus making it more tractable to maintain beliefs over the future trajectories of other agents towards plausible goals. The speed of inference and the effectiveness of the algorithms is demonstrated via physical robot experiments, where computationally constrained robots navigate amongst humans in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as 100ms after the first observation. While intention inference is a key aspect of successful human-robot interaction, executing any task requires planning that takes into account the predicted goals and trajectories of other agents, e.g., pedestrians. It is well known that robots demonstrate unwanted behaviours, such as freezing or becoming sluggishly responsive, when placed in dynamic and cluttered environments, due to the way in which safety margins according to simple heuristics end up covering the entire feasible space of motion. The presented approach makes more refined predictions about future movement, which enables robots to find collision-free paths quickly and efficiently. This thesis describes a novel technique for generating "interactive costmaps", a representation of the planner’s costs and rewards across time and space, providing an autonomous robot with the information required to navigate socially given the estimate of other agents’ intentions. This multi-layered costmap deters the robot from obstructing while encouraging social navigation respectful of other agents’ activity. Results show that this approach minimises collisions and near-collisions, minimises travel times for agents, and importantly offers the same computational cost as the most common costmap alternatives for navigation. A key part of the practical deployment of such technologies is their ease of implementation and configuration. Since every use case and environment is different and distinct, the presented methods use online adaptation to learn parameters of the navigating agents during runtime. Furthermore, this thesis includes a novel technique for allocating tasks in distributed robotics systems, where a tool is provided to maximise the performance on any distributed setup by automatic parameter tuning. All of these methods are implemented in ROS and distributed as open-source. The ultimate aim is to provide an accessible and efficient framework that may be seamlessly deployed on modern robots, enabling widespread use of intention prediction for interactive navigation in distributed robotic systems.

O problema da localização e mapeamento simultâneos, conhecido como problema do SLAM, é um dos maiores desafios que a robótica móvel autônoma enfrenta atualmente. Esse problema surge devido à dificuldade que um robô apresenta ao navegar por um ambiente desconhecido, construindo um mapa das regiões por onde já passou ao mesmo tempo em que se localiza dentro dele. O acúmulo de erros gerados pela imprecisão dos sensores utilizados para estimar os estados de localização e mapeamento impede que sejam obtidos resultados confiáveis após períodos de navegação suficientemente longos. Algoritmos de SLAM procuram eliminar esses erros resolvendo ambos os problemas simultaneamente, utilizando as informações de uma etapa para aumentar a precisão dos resultados alcançados na outra e viceversa. Uma das maneiras de se alcançar isso se baseia no estabelecimento de marcos no ambiente que o robô pode utilizar como pontos de referência para se localizar conforme navega. Esse trabalho apresenta uma solução para o problema do SLAM que faz uso de um sensor de visão omnidirecional para estabelecer esses marcos. O uso de sistemas de visão permite a extração de marcos naturais ao ambiente que podem ser correspondidos de maneira robusta sob diferentes pontos de vista. A visão omnidirecional amplia o campo de visão do robô e com isso aumenta a quantidade de marcos observados a cada instante. Ao ser detectado o marco é adicionado ao mapa que robô possui do ambiente e, ao ser reconhecido, o robô pode utilizar essa informação para refinar suas estimativas de localização e mapeamento, eliminando os erros acumulados e conseguindo mantê-las precisas mesmo após longos períodos de navegação. Essa solução foi testada em situações reais de navegação, e os resultados mostram uma melhora significativa nos resultados alcançados em relação àqueles obtidos com a utilização direta das informações coletadas. / The problem of simultaneous localization and mapping, known as the problem of SLAM, is one of the greatest obstacles that the field of autonomous robotics faces nowadays. This problem is related to a robots ability to navigate through an unknown environment, constructing a map of the regions it has already visited at the same time as localizing itself on this map. The imprecision inherent to the sensors used to collect information generates errors that accumulate over time, not allowing for a precise estimation of localization and mapping when used directly. SLAM algorithms try to eliminate these errors by taking advantage of their mutual dependence and solving both problems simultaneously, using the results of one step to refine the estimatives of the other. One possible way to achieve this is the establishment of landmarks in the environment that the robot can use as points of reference to localize itself while it navigates. This work presents a solution to the problem of SLAM using an omnidirectional vision system to detect these landmarks. The choice of visual sensors allows for the extraction of natural landmarks and robust matching under different points of view, as the robot moves through the environment. The omnidirectional vision amplifies the field of vision of the robot, increasing the number of landmarks observed at each instant. The detected landmarks are added to the map, and when they are later recognized they generate information that the robot can use to refine its estimatives of localization and mapping, eliminating accumulated errors and keeping them precise even after long periods of navigation. This solution has been tested in real navigational situations and the results show a substantial improvement in the results compared to those obtained through the direct use of the information collected.

The master’s thesis implements localization and navigation routines for mobile robot B2 in order to operate autonomously in an environment described by a road map only. The ROS framework was used for developing new software. The research part describes possible approaches to localization problem and summarizes ROS packages with localization and navigation software. The following part includes communication with the robot’s sensor modules and data processing from LIDAR, IMU and camera. The localization package robot_localization based on Kalman filter is implemented and setting of the navigation stack navigation is proposed, aiming to robot’s autonomous outdoor navigation. Implemented functions were tested in park environment and they are evaluated in this master's thesis too.

This master’s thesis deals with the navigation of a mobile robot that uses the ROS framework. The aim is to improve the ability of the existing B2 robot to move autonomously outdoors. The theoretical part contains a description of the navigation core, which consists of the move_base library and the packages used for planning. The practical part describe the aws of the existing solution, the design and implementation of changes and the results of subsequent testing in the urban park environment.

Actualment, no hi ha consens científic respecte a la informació representada en la activitat de les célules del hipocamp. D'una banda, experiments amb humans sostenen una visión de la funció de l'hipocamp com a un sistema per l'emmagatzematge de memóries episódiques, mentre que la recerca amb rodents enfatitza una visió com a sistema cognitiu espacial. Tot i que existeix abundant evidència experimental que indica una possible sobreposició d'ambdues teories, aquesta dissociació també es manté en part en base a dades fisiològiques aparentment incompatibles. Aquesta tèsi poposa que l'hippocamp té un rol funcional que s'hauría d'analitzar en termes de la seva estructura i funció, enlloc de mitjança estudis correlació entre activitat neuronal i comportament. La identificació d'un codi a l'hipocamp, es a dir, el conjunt de principis computacionals que conformen les transformacions d'entrada i sortida de l'activitat neuronal, hauría de proporcionar un explicació unificada de la seva funció. En aquesta tèsi presentem un model teòric que descriu quantitativament i que interpreta la selectivitat de certes regions de l'hipocamp en funció de variables espaials i no-espaials, tal i com observada en experiments amb rates. Aquest resultat suggereix que multiples aspectes de la memòria expressada en humans i rodents deriven d'uns mateixos principis. Per aquest motius, proposem nous principis per la memòria, l'auto-completat de patrons i plasticitat. A més, mitjançant aplicacions robòtiques, creem d'un nexe causal entre el circuit neural i el comportament amb el que demostrem la naturalesa conjuntiva de la selectivitat neuronal observada en el hipocamp es necessària per la solució de problemes pràctics comuns, com per example la cerca d'aliments. Tot plegat, aquests resultats avancen en l'idea general de que el codi de l'hipocamp es genèric i aplicable als diversos tipus de memòries estudiades en la literatura. / There is no consensual understanding on what the activity of the hippocampus neurons represents. While experiments with humans foster a dominant view of an episodic memory system, experiments with rodents promote its role as a spatial cognitive system. Although there is abundant evidence pointing to an overlap between these two theories, the dissociation is sustained by conflicting physiological data. This thesis proposes that the functional role of the hippocampus should be analyzed in terms of its structure and function rather than by the correlation of neuronal activity and behavioral performance. The identification of the hippocampus code, i.e. the set of computational principles underlying the input-output transformations of neural activity, might ultimately provide a unifying understanding of its role. In this thesis we present a theoretical model that quantitatively describes and interprets the selectivity of regions of the hippocampus to spatial and non-spatial variables observed in experiments with rats. The results suggest that the multiple aspects of memory expressed in human and rodent data are derived form similar principles. This approach suggests new principles for memory, pattern completion and plasticity. In addition, by creating a causal tie between the neural circuitry and behavior through a robotic control framework we show that the conjunctive nature of neural selectivity observed in the hippocampus is needed for effective problem solving in real-world tasks such as foraging. Altogether, these results advance the concept that the hippocampal code is generic to the different aspects of memory highlighted in the literature.

Dans ces travaux, nous proposons un système sûr pour la localisation de robot mobile en milieu intérieur structuré. Le principe repose sur l’utilisation de deux capteurs (lidar et caméra monoculaire) combinés astucieusement pour assurer une rapidité de calcul et une robustesse d’utilisation. En choisissant des capteurs reposant sur des principes physiques différents, les chances qu'ils se retrouvent simultanément perturbés sont minimes. L’algorithme de localisation doit être rapide et efficient tout en conservant la possibilité de fournir un mode dégradé dans éventualité où l’un des capteurs serait endommagé. Pour atteindre cet objectif de localisation rapide, nous optimisons le traitement des données à divers niveaux tels que la quantité de données à traiter ou l’optimisation algorithmique. Nous opérons une approximation polygonale des données du lidar 2D ainsi qu’une détection des segments verticaux dans l’image couleur. Le croisement de ces deux informations, à l’aide d’un filtre de Kalman étendu, nous donne alors une localisation fiable. En cas de perte du lidar, le filtre de Kalman peut toujours fonctionner et, en cas de perte de la caméra, le robot peut faire un recalage laser avec le lidar. Les données des deux capteurs peuvent également servir à d’autres objectifs. Les données lidar permettent d’identifier les portes (points de collision potentiels avec des humains), les données caméra peuvent permettre la détection et le suivi des piétons. Les travaux ont été majoritairement menés et validés avec un simulateur robotique avancé (4DV-Sim) puis ont été confirmés par des expériences réelles. Cette méthodologie permet à la fois de développer nos travaux et de valider et améliorer le caractère fonctionnel de cet outil de robotique. / In this work, we propose a safe system for robot navigation in an indoor and structured environment. The main idea is the use of two combined sensors (lidar and monocular camera) to ensure fast computation and robustness. The choice of these sensors is based on the physic principles behind their measures. They are less likely to go blind with the same disturbance. The localization algorithm is fast and efficient while keeping in mind the possibility of a downgraded mode in case of the failure of one sensor. To reach this objective, we optimized the data processing at different levels. We applied a polygonal approximation to the 2D lidar data and a vertical contour detection to the colour image. The fusion of these data in an extended Kalman filter provides a reliable localization system. In case of a lidar failure, the Kalman filter still works, in case of a camera failure the robot can rely upon a lidar scan matching. Data provided by these sensors can also deserve other purposes. The lidar provides us the localization of doors, potential location for encounter with humans. The camera can help to detect and track humans. This work has been done and validated using an advanced robotic simulator (4DV-Sim), then confirmed with real experiments. This methodology allowed us to both develop our ideas and confirm the usefulness of this robotic tool.

Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Simultaneous localization and mapping (SLAM) is vital for autonomous robot navigation. The robot must build a map of its environment while tracking its own motion through that map. Although many solutions to this intricate problem have been proposed, one of the most prominent issues that still needs to be resolved is to accurately measure and track landmarks over time. In this thesis we investigate the use of stereo vision for this purpose. In order to find landmarks in images we explore the use of two feature detectors: the scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). Both these algorithms find salient points in images and calculate a descriptor for each point that is invariant to scale, rotation and illumination. By using the descriptors we match these image features between stereo images and use the geometry of the system to calculate a set of 3D landmark measurements. A Taylor approximation of this transformation is used to derive a Gaussian noise model for the measurements. The measured landmarks are matched to landmarks in a map to find correspondences. We find that this process often incorrectly matches ambiguous landmarks. To find these mismatches we develop a novel outlier detection scheme based on the random sample consensus (RANSAC) framework. We use a similarity transformation for the RANSAC model and derive a probabilistic consensus measure that takes the uncertainties of landmark locations into account. Through simulation and practical tests we find that this method is a significant improvement on the standard approach of using the fundamental matrix. With accurately identified landmarks we are able to perform SLAM. We investigate the use of three popular SLAM algorithms: EKF SLAM, FastSLAM and FastSLAM 2. EKF SLAM uses a Gaussian distribution to describe the systems states and linearizes the motion and measurement equations with Taylor approximations. The two FastSLAM algorithms are based on the Rao-Blackwellized particle filter that uses particles to describe the robot states, and EKFs to estimate the landmark states. FastSLAM 2 uses a refinement process to decrease the size of the proposal distribution and in doing so decreases the number of particles needed for accurate SLAM. We test the three SLAM algorithms extensively in a simulation environment and find that all three are capable of very accurate results under the right circumstances. EKF SLAM displays extreme sensitivity to landmark mismatches. FastSLAM, on the other hand, is considerably more robust against landmark mismatches but is unable to describe the six-dimensional state vector required for 3D SLAM. FastSLAM 2 offers a good compromise between efficiency and accuracy, and performs well overall. In order to evaluate the complete system we test it with real world data. We find that our outlier detection algorithm is very effective and greatly increases the accuracy of the SLAM systems. We compare results obtained by all three SLAM systems, with both feature detection algorithms, against DGPS ground truth data and achieve accuracies comparable to other state-of-the-art systems. From our results we conclude that stereo vision is viable as a sensor for SLAM. / AFRIKAANSE OPSOMMING: Gelyktydige lokalisering en kartering (simultaneous localization and mapping, SLAM) is ’n noodsaaklike proses in outomatiese robot-navigasie. Die robot moet ’n kaart bou van sy omgewing en tegelykertyd sy eie beweging deur die kaart bepaal. Alhoewel daar baie oplossings vir hierdie ingewikkelde probleem bestaan, moet een belangrike saak nog opgelos word, naamlik om landmerke met verloop van tyd akkuraat op te spoor en te meet. In hierdie tesis ondersoek ons die moontlikheid om stereo-visie vir hierdie doel te gebruik. Ons ondersoek die gebruik van twee beeldkenmerk-onttrekkers: scale-invariant feature transform (SIFT) en speeded-up robust features (SURF). Altwee algoritmes vind toepaslike punte in beelde en bereken ’n beskrywer vir elke punt wat onveranderlik is ten opsigte van skaal, rotasie en beligting. Deur die beskrywer te gebruik, kan ons ooreenstemmende beeldkenmerke soek en die geometrie van die stelsel gebruik om ’n stel driedimensionele landmerkmetings te bereken. Ons gebruik ’n Taylor- benadering van hierdie transformasie om ’n Gaussiese ruis-model vir die metings te herlei. Die gemete landmerke se beskrywers word dan vergelyk met dié van landmerke in ’n kaart om ooreenkomste te vind. Hierdie proses maak egter dikwels foute. Om die foutiewe ooreenkomste op te spoor het ons ’n nuwe uitskieterherkenningsalgoritme ontwikkel wat gebaseer is op die RANSAC-raamwerk. Ons gebruik ’n gelykvormigheidstransformasie vir die RANSAC-model en lei ’n konsensusmate af wat die onsekerhede van die ligging van landmerke in ag neem. Met simulasie en praktiese toetse stel ons vas dat die metode ’n beduidende verbetering op die standaardprosedure, waar die fundamentele matriks gebruik word, is. Met ons akkuraat geïdentifiseerde landmerke kan ons dan SLAM uitvoer. Ons ondersoek die gebruik van drie SLAM-algoritmes: EKF SLAM, FastSLAM en FastSLAM 2. EKF SLAM gebruik ’n Gaussiese verspreiding om die stelseltoestande te beskryf en Taylor-benaderings om die bewegings- en meetvergelykings te lineariseer. Die twee FastSLAM-algoritmes is gebaseer op die Rao-Blackwell partikelfilter wat partikels gebruik om robottoestande te beskryf en EKF’s om die landmerktoestande af te skat. FastSLAM 2 gebruik ’n verfyningsproses om die grootte van die voorstelverspreiding te verminder en dus die aantal partikels wat vir akkurate SLAM benodig word, te verminder. Ons toets die drie SLAM-algoritmes deeglik in ’n simulasie-omgewing en vind dat al drie onder die regte omstandighede akkurate resultate kan behaal. EKF SLAM is egter baie sensitief vir foutiewe landmerkooreenkomste. FastSLAM is meer bestand daarteen, maar kan nie die sesdimensionele verspreiding wat vir 3D SLAM vereis word, beskryf nie. FastSLAM 2 bied ’n goeie kompromie tussen effektiwiteit en akkuraatheid, en presteer oor die algemeen goed. Ons toets die hele stelsel met werklike data om dit te evalueer, en vind dat ons uitskieterherkenningsalgoritme baie effektief is en die akkuraatheid van die SLAM-stelsels beduidend verbeter. Ons vergelyk resultate van die drie SLAM-stelsels met onafhanklike DGPS-data, wat as korrek beskou kan word, en behaal akkuraatheid wat vergelykbaar is met ander toonaangewende stelsels. Ons resultate lei tot die gevolgtrekking dat stereo-visie ’n lewensvatbare sensor vir SLAM is.