Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

Meta-learning based infrared ship object detection model for generalization to unknown domains

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploitation of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance

Integrated metric-topological localization by fusing visual odometry, digital map and place recognition

Visual odometry, map-assisted methods and place recognition are all popular approaches to localize a mobile vehicle from three different perspectives. Separate implementation of these methods may cause the localization system vulnerable due to the drift issue and local pose estimation of visual odometry, the on-road assumption and tough initialization of map-assisted methods and the discontinuous output of place recognition. In order to give full play to their advantages, an integrated localization strategy is presented in this paper, where metric data such as visual odometry measurement, a digital map and topological data of place recognition results are incorporated. Place recognition assists initialization process and provides topological place estimation at all times. Gaussian-Gaussian Distribution is used for visual odometry raw measurement representation such that the errors of odometry is appropriately modelled. By comparing similarities between the digital map and odometry trajectories, we then use map-assisted approach to correct odometry estimation. Finally, a mutual check gives a criterion for judging whether metric and topological results are sufficiently consistent. Experiment results show that the integrated system outperforms subsystems with mean localization error at 2.9 m on our self-collected dataset with off-road scenarios

Heading reference-assisted pose estimation for ground vehicles

In this paper, heading reference-assisted pose estimation (HRPE) has been proposed to compensate inherent drift of visual odometry (VO) on ground vehicles, where an estimation error is prone to grow while the vehicle is making turns or in environments with poor features. By introducing a particular orientation as “heading reference,” a pose estimation framework has been presented to incorporate measurements from heading reference sensors into VO. A graph formulation is then proposed to represent the pose estimation problem under the commonly used graph optimization model. Simulations and experiments on KITTI data set and our self-collected sequences have been conducted to verify the accuracy and robustness of the proposed scheme. KITTI sequences and manually generated heading measurement with Gaussian noises are used in simulation, where rotational drift error is observed to be bounded. Compared with a pure VO, the proposed approach greatly reduces average translational localization error from 153.85 to 24.29 m and 23.80 m in self-collected stereo visual sequences with traveling distance over 4.5 km at the processing rates of 19.7 and 11.1 Hz, for the loosely coupled and tightly coupled models, respectively.Accepted versio

Deep Transfer Learning for Wall Bulge Endpoints Regression for Autonomous Decoration Robots

Wall bulge maintenance and repairing is an essential task for autonomous decoration robots. The problem of the wall bulge endpoints regression refers to identifying the position of the wall bulge endpoints in spatial coordinates. This problem is of significant importance for autonomous decoration robots as these robots target automatic maintenance and repairing of wall bulges, they must automatically recognize where to start and stop the repairing process. Training deep convolutional neural networks for supervised computer vision tasks requires a large number of annotated images. Since gathering annotated images for this task is difficult, laborious, and time-consuming, we proposed a model for detecting the wall bulge endpoints position based on deep transfer learning. Our proposed model is capable of classifying the wall bulge into one of four classes according to its orientation. Deep transfer learning transfers the knowledge acquired by deep learning models trained for a specific task and domain to another different but related task and domain. Our proposed model is mainly based on deep convolutional neural networks pre-trained on large datasets for tasks of object classification and detection. We transfer the knowledge acquired by the model from these tasks to solve both problems in our new task

Secure estimation for attitude and heading reference systems under sparse attacks

This paper focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established AHRS measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then propose a convex optimization-based approach, instead of the weighted sum approach, to combine the local estimate into a more secure estimate. It is shown that the proposed secure estimator coincides with the Kalman estimator with certain probability when there is no attack, and can be stable when p elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks

Maximum entropy searching

This study presents a new perspective for autonomous mobile robots path searching by proposing a biasing direction towards causal entropy maximisation during random tree generation. Maximum entropy-biased rapidly-exploring random tree (ME-RRT) is proposed where the searching direction is computed from random path sampling and path integral approximation, and the direction is incorporated into the existing rapidly-exploring random tree (RRT) planner. Properties of ME-RRT including degenerating conditions and additional time complexity are also discussed. The performance of the proposed approach is studied, and the results are compared with conventional RRT/RRT* and goal-biased approach in 2D/3D scenarios. Simulations show that trees are generated efficiently with fewer iteration numbers, and the success rate within limited iterations has been greatly improved in complex environments