End-to-End Navigation in Unknown Environments using Neural Networks

We investigate how a neural network can learn perception actions loops for navigation in unknown environments. Specifically, we consider how to learn to navigate in environments populated with cul-de-sacs that represent convex local minima that the robot could fall into instead of finding a set of feasible actions that take it to the goal. Traditional methods rely on maintaining a global map to solve the problem of over coming a long cul-de-sac. However, due to errors induced from local and global drift, it is highly challenging to maintain such a map for long periods of time. One way to mitigate this problem is by using learning techniques that do not rely on hand engineered map representations and instead output appropriate control policies directly from their sensory input. We first demonstrate that such a problem cannot be solved directly by deep reinforcement learning due to the sparse reward structure of the environment. Further, we demonstrate that deep supervised learning also cannot be used directly to solve this problem. We then investigate network models that offer a combination of reinforcement learning and supervised learning and highlight the significance of adding fully differentiable memory units to such networks. We evaluate our networks on their ability to generalize to new environments and show that adding memory to such networks offers huge jumps in performanceComment: Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory and Efficiency, Robotics Science and Systems 201

2D SLAM Correction Prediction in Large Scale Urban Environments

International audienceSimultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations

A review of Kalman filter with artificial intelligence techniques

Kalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called "black box". In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance. Based on the review, we categorised papers into four groups according to the role of AI as follows: 1) Methods tuning parameters of KF, 2) Methods compensating errors in KF, 3) Methods updating state vector or measurements of KF, and 4) Methods estimating pseudo-measurements of KF. In the concluding section of this paper, we pointed out the directions for future research that suggestion to focus on more research for combining the categorised groups. In addition, we presented the suggestion of beneficial approaches for representative applications

Using the optical flow in the visual odometry applied robotics

Orientador: Paulo Roberto Gardel KurkaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O presente trabalho descreve um método de odometria visual empregando a técnica de fluxo óptico, para estimar o movimento de um robô móvel, através de imagens digitais capturadas de duas câmeras estereoscópicas nele fixadas. Busca-se assim a construção de um mapa para a localização do Robô. Esta proposta, além de alternativa ao cálculo autônomo de movimento realizado por outros tipos de sensores como GPS, laser, sonares, utiliza uma técnica de processamento óptico de grande eficiência computacional. Foi construído um ambiente 3D para simulação do movimento do robô e captura das imagens necessárias para estimar sua trajetória e verificar a acurácia da técnica proposta. Utiliza-se a técnica de fluxo óptico de Lucas Kanade na identificação de características em imagens. Os resultados obtidos neste trabalho são de grande importância para os estudos de navegação robóticaAbstract: This work describes a method of visual odometry using the optical flow technique to estimate the motion of a mobile robot, through digital images captured from two stereoscopic cameras fixed on it, in order to obtain a map of location of the robot. This proposal is an alternative to the autonomous motion calculation performed by other types of sensors such as GPS, laser, sonar, and uses an optical processing technique of high computational efficiency. To check the accuracy of the technique it was necessary to build a 3D environment to simulate the robot performing a trajectory and capture the necessary images to estimate the trajectory. The optical flow technique of Lucas Kanade was used for identifying features in the images. The results of this work are of great importance for future robotic navigation studiesMestradoMecanica dos Sólidos e Projeto MecanicoMestra em Engenharia Mecânic

Errors and Truths from Transportation Data Aggregation: Some Implications for Research and Practice

Data aggregation, which is a process to combine information by defined groups for statistical analysis, summary, data size reduction, or other purposes, has fundamental challenges, such as loss of the original information. Improper data aggregation, such as sampling bias or incorrect calculation of average, may cause misreading of information. In first chapter, it is revealed that the harmonic mean, which is used to calculate space mean speed for fixed segment, has a sampling bias, i.e., overestimation with small samples. The several impact analyses show that the sampling bias is affected by sampling rate, time interval, segment length, and distribution type. If the data aggregation is properly used, it can help us improve analytical efficiency, encounter some of critical problems, or reveal its casualties and other relevant information. Second and third chapters utilize the aggregation of multi-source data to estimate error distributions of data sources and improve accuracy of their measurements. This is a leaping point of evaluating data sources as the proposed model does not require ground truth data. Second chapter focuses more on the methodology, i.e., a modified Approximate Bayesian Computation, incorporated to construct the error distribution with numerous simulations. In the simulated experiment, the proposed model outperformed the alternative approach, which is a conventional way of evaluating data source that is gathering error information by comparing with ground data source. Several sensitivity analyses explore that how the model performance is affected by sample size, number of data sources, and distribution types. The proposed model in chapter II is limited to one dimensional variable, and then the application is expanded to improving the position and distance measurement of connected vehicle environment. The proposed model can be used to further improve the accuracy of vehicle positioning with other existing methods, such as simultaneous localization and mapping (SLAM). The estimation process can be conducted in real-time operation, and the learning process will try to keep improving the accuracy of estimation. The results show that the proposed model noticeably improves the accuracy of position and distance measurements

Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments

To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors

Neural Network-Aided Extended Kalman Filter for SLAM Problem

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Algorithmic Robot Design: Label Maps, Procrustean Graphs, and the Boundary of Non-Destructiveness

This dissertation is focused on the problem of algorithmic robot design. The process of designing a robot or a team of robots that can reliably accomplish a task in an environment requires several key elements. How the problem is formulated can play a big role in the design process. The ability of the model to correctly reflect the environment, the events, and different pieces of the problem is crucial. Another key element is the ability of the model to show the relationship between different designs of a single system. These two elements can enable design algorithms to navigate through the space of all possible designs, and find a set of solutions. In this dissertation, we introduce procrustean graphs, a model for encoding the robot-environment interactions. We also provide a model for navigating through the space of all possible designs, called label maps. Using these models, we focus on answering the following questions: What degradations to the set of sensors or actuators of a robotic system can be tolerated? How different degradations affect the cost of doing a given task? What sets of resources — that is, sensors and actuators — are minimal for accomplishing a specific given job? And how to find such a set? To this end, our general approach is to sample, using a variety of sampling methods, over the space of all maps for a given problem, and use different techniques for answering these questions. We use decision tree classifiers to determine the crucial sensors and actuators required for a robotic system to accomplish its job. We present an algorithm based on space bisection to find the boundary between the feasible and infeasible subspaces of possible designs. We present an algorithm to measure the cost of doing a given task, and another algorithm to find the relationship between different degradation of a robotic system and the cost of doing the task. In all these solutions, we use a variety of techniques to scale up each approach to enable it to solve real world problems. Our experiments show the efficiency of the presented approach

Navigation with Artificial Neural Networks

The objective of this dissertation is to explore the applications for Artificial Neural Networks (ANNs) in the field of Navigation. The state of the art for ANNs has improved significantly so now they can rival and even surpass humans in problems once thought impossible. We present different methods to augment, combine, or replace existing Navigation filters with ANN. The main focus of these methods is to use as much existing knowledge as possible then use ANNs to extend the current knowledge base. Next, improvements are made for a class of Artificial Neural Network (ANN)s which provide covariance called Mixture Density Network (MDN)s. MDNs are necessary since covariance is required for navigation problems. Finally the improvements and framework are demonstrated in a Very Low Frequency (VLF) signals navigation problem. Without ANNs, our VLF signals navigation problem would be very difficult. We conduct two VLF navigation experiments with an indoor and outdoor environment. The ANNs used for these problems provide confidence with probabilistic estimates of position either through class probabilities or probability distributions parameterized by the output of MDNs. ANNs need a measure of confidence in their estimates to work with the filters since navigation filters require a confidence of their estimates. In our problems we achieve an indoor localization accuracy of 86.7% for 50 discrete locations, and a 2D RMS error of 63m for a 1km2 area of navigation