Benchmarking and Comparing Popular Visual SLAM Algorithms

This paper contains the performance analysis and benchmarking of two popular visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The dataset selected has a large set of image sequences from a Microsoft Kinect RGB-D sensor with highly accurate and time-synchronized ground truth poses from a motion capture system. The test sequences selected depict a variety of problems and camera motions faced by Simultaneous Localization and Mapping (SLAM) algorithms for the purpose of testing the robustness of the algorithms in different situations. The evaluation metrics used for the comparison are Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis involves comparing the Root Mean Square Error (RMSE) of the two metrics and the processing time for each algorithm. This paper serves as an important aid in the selection of SLAM algorithm for different scenes and camera motions. The analysis helps to realize the limitations of both SLAM methods. This paper also points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure

Visual Odometry by Multi-frame Feature Integration

This paper presents a novel stereo-based visual odometry approach that provides state-of-the-art results in real time, both indoors and outdoors. Our proposed method follows the procedure of computing optical flow and stereo disparity to minimize the re-projection error of tracked feature points. However, instead of following the traditional approach of performing this task using only consecutive frames, we propose a novel and computationally inexpensive technique that uses the whole history of the tracked feature points to compute the motion of the camera. In our technique, which we call multi-frame feature integration, the features measured and tracked over all past frames are integrated into a single, improved estimate. An augmented feature set, composed of the improved estimates, is added to the optimization algorithm, improving the accuracy of the computed motion and reducing ego-motion drift. Experimental results show that the proposed approach reduces pose error by up to 65 % with a negligible additional computational cost of 3.8%. Furthermore, our algorithm outperforms all other known methods on the KITTI Vision Benchmark data set. 1

Efficient calibration of four wheel industrial AGVs

In this paper, we propose a novel method for extrinsic and intrinsic automatic calibration of four wheel industrial Automated Guided Vehicles (AGVs) compliant with Ackermann and Dual Drive kinematics. For each kinematic model the algorithm estimates the trajectories measured by an on-board sensor and the expected ones given the state of the wheels. The estimation exploits the model equations derived in this work which constrain calibration parameters and measurements from wheel encoders and sensor odometry. The parameter values are computed through closed-form solutions of least-square estimation. The method has been implemented on Programmable Logic Controllers and tested on industrial AGVs. The developed procedure computes the parameters in about 10−15 minutes, a significant improvement compared with one hour or more required by manual AGV calibration. Experiments with AGVs of various sizes in a warehouse have assessed the accuracy and stability of the proposed approach. The position accuracy achieved by AGVs calibrated with the proposed method is higher than the one achieved by manual calibration

Slip Modeling and Estimation for a Planetary Exploration Rover: Experimental Results from Mt. Etna

For wheeled mobile systems, the wheel odometry is an important source of information about the current motion of the vehicle. It is used e.g. in the context of pose estimation and self-localization of planetary rovers, which is a crucial part of the success of planetary exploration missions. Depending on the wheel-soil interaction properties, wheel odometry measurements are subject to inherent errors such as wheel slippage. In this paper, a parameter-based approach for whole-body slip modeling and calibration is applied to a four-wheeled lightweight rover system. Details on the method for slip parameter calibration as well as the system-specific implementation are given. Experimental results from a test campaign on Mt. Etna are presented, showing significant improvements of the resulting wheel odometry measurements. The results are validated during a long range drive of approx. 900 m and discussed w. r. t. the advantages but also limitations of the method within a space exploration scenario

Path Tracking for a Skid-steer Vehicle using Learning-based Model Predictive Control

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2017. 2. 김현진.Skid-steer vehicle can generate a large traction force, which is especially good for navigation on a rough terrain. However, the turning motion is so sensitive to slippage effect that designing a controller is still a challenging problem. Also, the motion of the vehicle is affected not only by wheel motion, but also by the road properties and the characteristics of wheel control. With this in mind, we employ a model predictive control (MPC) with an on-line model learning. The velocity model, which represents the relationship between true vehicle velocity and input command, is learned with an on-line sparse Gaussian process (GP). The on-line sparse GP can reduce the computational complexity of GP and also consistently update the model from the driving data. Finally, combining with MPC makes it possible to generate an optimal policy based on the learned model. Experiments are conducted to test the tracking performance of a skid-steer robot at the indoor and the outdoor environment. The results show the more reliable performance than the method based on a conventional model with parameter adaptation.1 Introduction 1 1.1 Literature review 1 1.2 Thesis contribution 3 1.3 Thesis outline 4 2 On-line sparse Gaussian process for velocity model 5 2.1 Kinematic model 5 2.2 Sparse Gaussian process 7 2.3 On-line updating 10 3 Model predictive control 11 3.1 Iterative linear quadratic regulator 11 3.2 Cost formulation 15 3.3 Summary of the algorithm 16 4 Experiments 18 4.1 Experimental setup 18 4.2 Indoor experimental results 22 4.3 Outdoor experimental results 29 5 Conclusion 32 5.1 Challenges and future works 32 References 34 국문초록 37Maste

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Exploiting graph structure in Active SLAM

Aplicando análisis provenientes de la teoría de grafos, la teoría espectral de grafos, la exploración de grafos en línea, generamos un sistema de SLAM activo que incluye la planificación de rutas bajo incertidumbre, extracción de grafos topológicos de entornos y SLAM activo \'optimo.En la planificación de trayectorias bajo incertidumbre, incluimos el análisis de la probabilidad de asociación correcta de datos. Reconociendo la naturaleza estocástica de la incertidumbre, demostramos que planificar para minimizar su valor esperado es más fiable que los actuales algoritmos de planificación de trayectorias con incertidumbre.Considerando el entorno como un conjunto de regiones convexas conectadas podemos tratar la exploración robótica como una exploración de grafos en línea. Se garantiza una cobertura total si el robot visita cada región. La mayoría de los métodos para segmentar el entorno están basados en píxeles y no garantizan que las regiones resultantes sean convexas, además pocos son algoritmos incrementales. En base a esto, modificamos un algoritmo basado en contornos en el que el entorno se representa como un conjunto de polígonos que debe segmentarse en un conjunto de polígonos pseudo convexos. El resultado es un algoritmo de segmentación que produjo regiones pseudo-convexas, robustas al ruido, estables y que obtienen un gran rendimiento en los conjuntos de datos de pruebas.La calidad de un algoritmo se puede medir en términos de cuan cercano al óptimo está su rendimiento. Con esta motivación definimos la esencia de la tarea de exploración en SLAM activo donde las únicas variables son la distancia recorrida y la calidad de la reconstrucción. Restringiendo el dominio al grafo que representa el entorno y probando la relación entre la matriz asociada a la exploración y la asociada al grafo subyacente, podemos calcular la ruta de exploración óptima.A diferencia de la mayoría de la literatura en SLAM activo, proponemos que la heurística para la exploración de grafos consiste en atravesar cada arco una vez. Demostramos que el tipo de grafos resultantes tiene un gran rendimiento con respecto a la trayectoria \'optima, con resultados superiores al 97 \% del \'optimo en algunas medidas de calidad.El algoritmo de SLAM activo TIGRE integra el algoritmo de extracción de grafos propuesto con nuestra versión del algoritmo de exploración incremental que atraviesa cada arco una vez. Nuestro algoritmo se basa en una modificación del algoritmo clásico de Tarry para la búsqueda en laberintos que logra el l\'imite inferior en la aproximación para un algoritmo incremental. Probamos nuestro sistema incremental en un escenario de exploración típico y demostramos que logra un rendimiento similar a los métodos fuera de línea y también demostramos que incluso el método \'optimo que visita todos los nodos calculado fuera de línea tiene un peor rendimiento que el nuestro.<br /

On the Uncertainty in Active SLAM: Representation, Propagation and Monotonicity

La localización y mapeo simultáneo activo (SLAM activo) ha recibido mucha atención por parte de la comunidad de robótica por su relevancia en aplicaciones de robot móviles. El objetivo de un algoritmo de SLAM activo es planificar la trayectoria del robot para maximizar el área explorada y minimizar la incertidumbre asociada con la estimación de la posición del robot. Durante la fase de exploración de un algoritmo de SLAM, donde el robot navega en una región previamente desconocida, la incertidumbre asociada con la localización del robot crece sin límites. Solo después de volver a visitar regiones previamente conocidas, se espera una reducción en la incertidumbre asociada con la localización del robot mediante la detección de cierres de bucle. Esta tesis doctoral se centra en la importancia de representar y cuantificar la incertidumbre para calcular correctamente la confianza asociada con la estimación de la localización del robot en cada paso de tiempo a lo largo de su recorrido y, por lo tanto, decidir la trayectoria correcta de acuerdo con el objetivo de SLAM activo.En la literatura, se han propuesto fundamentalemente dos tipos de modelos de representación de la incertidumbre: absoluta y diferencial. En representación absoluta, la información sobre la incertidumbre asociada con la localización del robot está representada por una función de distribución de probabilidad, generalmente gausiana, sobre las variables de localización absoluta con respecto a una referencia base elegida. La estimación de la posición del robot está dada por la esperanza de las variables asociadas con la localización y la incertidumbre por su matriz de covarianza asociada. La representación diferencial utiliza una representación local de la incertidumbre, la posición estimada del robot se representa mediante la mejor aproximación de la posición absoluta y el error de estimación se representa localmente mediante un vector diferencial. Este vector generalmente también está representado por una función de distribución de probabilidad gausiana. Representaciones equivalentes al modelo diferencial han utilizado las herramientas de Grupos de Lie y Álgebras de Lie para representar la incertidumbre. Además de estos modelos, existen diferentes formas de representar la posición y orientación de la posición del robot, ángulos de Euler, cuaterniones y transformaciones homogéneas.Los enfoques más comunes para cuantificar la incertidumbre en SLAM se basan en criterios de optimalidad con el objetivo de cuantificar el mapa y la incertidumbre de la posición del robot: A-opt (traza de la matriz de covarianza, o suma de sus autovalores), D-opt (determinante de la matriz de covarianza, o producto de sus autovalores) y E-opt (criterio del mayor autovalor). Alternativamente, otros algoritmos de SLAM activo, basados en la Teoría de la Información, se basan en el uso de la entropía de Shannon para seleccionar acciones que lleven al robot al objetivo seleccionado. En un escenario de SLAM activo, garantizar la monotonicidad de estos criterios en la toma de decisiones durante la exploración, es decir, cuantificar correctamente que la incertidumbre encapsulada en una matriz de covarianza está aumentando, es un paso esencial para tomar decisiones correctas. Como ya se ha mencionado, durante la fase de exploración la incertidumbre asociada con la localización del robot aumenta. Por lo tanto, si no se preserva la monotonicidad de los criterios considerados, el sistema puede seleccionar trayectorias o caminos que creen falsamente que conducen a una menor incertidumbre de la localización del robot.En esta tesis, revisamos el trabajo relacionado sobre representación y propagación de la incertidumbre de la posición del robot en los diferentes modelos propuestos en la literatura. Además, se lleva a cabo un análisis de la incertidumbre representada localmente con un vector diferencial y la incertidumbre representada usando grupos de Lie. Investigamos la monotonicidad de diferentes criterios para la toma de decisiones, tanto en 2D como en 3D, dependiendo de la representación de la incertidumbre y de la representación de la orientación del robot. Nuestra conclusión fundamental es que la representación de la incertidumbre sobre grupos de Lie y usando un vector diferencial son similares e independientes de la representación utilizada para la parte rotacional de la posición del robot. Esto se debe a que la incertidumbre se representa localmente en el espacio de las transformaciones diferenciales que se corresponde con el álgebra de Lie del grupo euclidiano especial SE(n). Sin embargo, en el espacio tridimensional, la estimación de la localización del robot depende de las diferentes formas de representación de la parte rotacional. Por lo tanto, una forma adecuada de manipular conjuntamente la estimación y la incertidumbre del robot es utilizando la teoría de grupos de Lie debido a que es una representación que garantiza propiedades tales como una representación mínima y libre de singularidades en los ángulos de rotación. Analíticamente, demostramos que, utilizando representaciones diferenciales para la propagación de la incertidumbre, la monotonicidad se conserva para todos los criterios de optimalidad, A-opt, D-opt y E-opt y para la entropía de Shannon. También demostramos que la monotonicidad no se cumple para ninguno de ellos en representaciones absolutas usando ángulos Roll-Pitch-Yaw y Euler. Finalmente, mostramos que al usar cuaterniones unitarios en representaciones absolutas, los únicos criterios que preservan la monotonicidad son D-opt y la entropía de Shannon.Estos hallazgos pueden guiar a los investigadores de SLAM activo a seleccionar adecuadamente un modelo de representación de la incertidumbre, de modo que la planificación de trayectorias y los algoritmos de exploración puedan evaluar correctamente la evolución de la incertidumbre asociada a la posición del robot.Active Simultaneous Localization and Mapping (Active SLAM) has received a lot of attention from the robotics community for its relevance in mobile robotics applications. The objective of an active SLAM algorithm is to plan ahead the robot motion in order to maximize the area explored and minimize the uncertainty associated with the estimation, all within a time and computation budget. During the exploration phase of a SLAM algorithm, where the robot navigates in a previously unknown region, the uncertainty associated with the robot's localization grows unbounded. Only after revisiting previously known regions a reduction in the robot's localization uncertainty is expected by detecting loop-closures. This doctoral thesis focuses on the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot's location estimate at each time step along its trajectory and therefore deciding the correct course of action in an active SLAM mission. Two fundamental types of models of probabilistic representation of the uncertainty have been proposed in the literature: absolute and dfferential. In absolute representations, the information about the uncertainty in the location of the robot's pose is represented by a probability distribution function, usually Gaussian, over the variables of the absolute location with respect to a chosen base reference. The estimated location is given by the expected location variables and the uncertainty by its associated covariance matrix. Differential representations use a local representation of the uncertainty, the estimated location of the robot is represented by the best approximation of the absolute location and the estimation error is represented locally by a differential location vector. This vector is usually also represented by a Gaussian probability distribution function. Equivalent representations to differential models have used the tools of Lie groups and Lie algebras to represent uncertainties. In addition to uncertainty models, there are different ways to represent the position and orientation of the robot's pose, Euler angles, quaternions and homogeneous transformations. The most common approaches to quantifying uncertainty in SLAM are based on optimality criteria which aim at quantifying the map and robot's pose uncertainty, namely A-opt (trace of the covariance matrix, or sum of its eigenvalues), D-opt (determinant of the covariance matrix, or product of its eigenvalues) and E-opt (largest eigenvalue) criteria. Alternatively, other active SLAM algorithms, based on Information Theory, rely on the use of the Shannon's entropy to select courses of action for the robot to reach the commanded goal location. In an active SLAM scenario, guaranteeing monotonicity of these decision making criteria during exploration, i.e. quantifying correctly that the uncertainty encapsulated in a covariance matrix is increasing, is an essential step towards making correct decisions. As already mentioned, during exploration the uncertainty associated with the robot's localization increases. Therefore, if monotonicity of the criteria considered is not preserved, the system might select courses of action or paths that it falsely believes lead to less uncertainty in the robot. In this thesis, we review related work about representation and propagation of the uncertainty of robot's pose and present a survey of different types of models proposed in the literature. Additionally, an analysis of the uncertainty represented with a differential uncertainty vector and the uncertainty represented on Lie groups is carried out. We investigate the monotonicity of different decision making criteria, both in 2D and 3D, depending on the representation of uncertainty and the orientation of the robot's pose. Our fundamental conclusion is that uncertainty representation over Lie groups and using differential location vectors are similar and independent of the representation used for rotational part of the robot's pose. This is due to the uncertainty is represented locally in the space of differential transformations for translation and rotation that correspond with the Lie algebra of special Euclidean group SE(n). However, in 3-dimensional space, the homogeneous transformation associated to the approximation of the real location depend on the different ways of representation the rotational part. Therefore, a proper way to jointly manipulating the estimation and uncertainty of the pose is to use the theory of Lie groups due to it is a representation to guarantee properties such as a minimal representation and free of singularities in rotation angles. We analytically show that, using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt and E-opt and for Shannon's entropy. We also show that monotonicity does not hold for any of them in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, we show that using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon's entropy. These findings can guide active SLAM researchers to adequately select a representation model for uncertainty, so that path planning and exploration algorithms can correctly assess the evolution of location uncertainty.<br /