Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Analysis and development of self localization and autonomous driving systems for the industrial and automotive environment.

The goal of the present thesis is dual. First the self localization problem is investigated in the industrial environment and a working prototype, consisting in an AGV able to perform self localization and autonomous driving under certain conditions, has been developed. Then the self localization problem is considered as a part of wider and currently very active research problem: the Simultaneous Localization And Mapping (SLAM), considered in the automotive environment.L'obiettivo della presente Tesi è duplice. Inizialmente il problema della self localization viene analizzato nel contesto industriale e un prototipo funzionante è stato realizzato, consistente in un AGV in grado di auto localizzarsi in certe condizioni. In seguito il problema della self localization è stato considerato come parte di un problema più ampio e ad oggi particolarmente: il Simultaneous Localization And Mapping (SLAM), nel contesto automobilistico

Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects

Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing). This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that incorporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on high-level language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.Ph.D

Formal Methods for Autonomous Systems

Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main building blocks of formal methods are models and specifications, which are analogous to behaviors and requirements in system design and give us the means to verify and synthesize system behaviors with formal guarantees. This monograph provides a survey of the current state of the art on applications of formal methods in the autonomous systems domain. We consider correct-by-construction synthesis under various formulations, including closed systems, reactive, and probabilistic settings. Beyond synthesizing systems in known environments, we address the concept of uncertainty and bound the behavior of systems that employ learning using formal methods. Further, we examine the synthesis of systems with monitoring, a mitigation technique for ensuring that once a system deviates from expected behavior, it knows a way of returning to normalcy. We also show how to overcome some limitations of formal methods themselves with learning. We conclude with future directions for formal methods in reinforcement learning, uncertainty, privacy, explainability of formal methods, and regulation and certification

Activity profiling for minimally invasive surgery

Imperial Users onl