Learning human motion prediction models online is key for autonomous navigation in unknown dynamic scenarios. Previous works focus solely on improving prediction network architectures, whilst training them offline. This paper introduces a self-supervised continual learning framework that continuously improves data-driven pedestrian trajectory prediction models online across various environments. We propose to use online streams of pedestrian data, normally available from detection and tracking pipelines. Examples are autonomously extracted from this data stream and aggregated in temporally bounded episodes, where the data of each episode is discarded as soon as the model has been adapted to it. Our framework overcomes the problem of catastrophic forgetting across episodes by selectively slowing down learning of important neurons and by rehearsing a small set of examples of constant length. Our approach is shown to significantly improve prediction performance in new and unseen environments compared standard gradient descent approaches. Finally, we present qualitative experimental results in simulation and in real environments.

With the performance of current motion planning methods being highly dependent on the quality of the perception system, robust 3D multi-object detection and tracking are vital for autonomous driving applications. Despite all the advancements in 2D and 3D object detectors, robust tracking of pedestrians in dense scenarios is still a challenging subject for small Automated Guided Vehicles (AGVs). Most research in the field of object detection and tracking focuses on autonomous cars, neglecting the design challenges that come with small AGVs.

This thesis presents a real-time multi-modal multi-pedestrian detection and tracking pipeline for small mobile robots. The framework integrates five RGB-D cameras and a LiDAR sensor to achieve real-time pedestrian detection and tracking. The system relies on state-of-the-art 2D and 3D object detectors, a sensor fusion and filtering scheme, and a 3D object tracker. Moreover, to improve detection and tracking performance, we have collected a pedestrian dataset tailored for small AGVs. We use this dataset to train the 3D pedestrian detector and evaluate the performance of the pedestrian detectors and tracker. Evaluation of the proposed framework demonstrated the ability to robustly detect and track multiple pedestrians up to a distance of 10 meters. We open-sourced our framework at: https://github.com/bbrito/amr_navigation.

A lot research has been conducted in the field of autonomous navigation of mobile robots with focus on Robot Vision and Robot Motion Planning. However, most of the classical navigation solutions require several steps of data pre-processing and hand tuning of parameters, with separate modules for vision, localization, planning and control. All these modules work independently and make their own parameter assumptions to optimize their own performance without taking into account the effect these assumptions have on the performance of rest of the modules. Hence, even though each module in the whole system tries to achieve an optimal performance for the task it has been assigned, the lack of interdependence exhibited by these modules for decision making means that the overall performance of the whole system is sub-optimal in most of the cases. An alternating approach for addressing these issues is to train certain parts of the vision module to incorporate partial tasks from the planning module. Deep Learning architectures have achieved great success in the field of pattern recognition and object detection and as a result are usually being deployed to design such a module that jointly learns to carry out perception and path planning. This master's thesis, making use of Deep Learning, proposes an End-to-End Learning architecture that learns to directly map raw sensor readings to control commands for a ground based mobile robot. The research makes use of the simulation of Jackal UGV from Clearpath Robotics and the proposed network is able to produce collision free trajectories for the robot to navigate in it's environment.

The successful integration of autonomous vehicles (AVs) in human environments is highly dependent on their ability to navigate safely and timely through dense traffic conditions. Such conditions involve a diverse range of human behaviors, ranging from cooperative (willing to yield) to non-cooperative human drivers (unwilling to yield) that need to be identified without any explicit inter-vehicle communication. In order to maneuver through such conditions, AVs must not only compute a collision-free trajectory but also account for the effects of its actions on the surrounding agents to negotiate the navigation maneuver safely. Existing motion planning techniques fail in these environments because they suffer from one or more of the following drawbacks: suffer from ”the curse of dimensionality” due to the high number of agents (e.g., optimization-based methods); do not account for the interaction effects among the agents; do not provide any collision avoidance or trajectory feasibility guarantees (e.g., learning-based methods). In this paper, we propose a novel navigation framework combining the strengths of learning-based with optimization-based algorithms. More specifically, we employ a Soft Actor-Critic agent to learn a continuous guidance policy that provides global guidance to an optimization-based planner generating feasible and collision- free trajectories. We evaluate our method in a highly inter- active simulation environment where we compare our method with two baseline approaches, a learning-based method and an optimization-based method, and present performance results demonstrating our method significantly reduces the number of collisions and increase the success rate with fewer number of deadlocks. We also show that that our method is able to generalise and applicable to other traffic scenarios (e.g., an unprotected left turn).