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

Intelligent Robotic Perception Systems

Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics

Improving Mobility and Safety in Traditional and Intelligent Transportation Systems Using Computational and Mathematical Modeling

In traditional transportation systems, park-and-ride (P&R) facilities have been introduced to mitigate the congestion problems and improve mobility. This study in the second chapter, develops a framework that integrates a demand model and an optimization model to study the optimal placement of P&R facilities. The results suggest that the optimal placement of P&R facilities has the potential to improve network performance, and reduce emission and vehicle kilometer traveled. In intelligent transportation systems, autonomous vehicles are expected to bring smart mobility to transportation systems, reduce traffic congestion, and improve safety of drivers and passengers by eliminating human errors. The safe operation of these vehicles highly depends on the data they receive from their external and on-board sensors. Autonomous vehicles like other cyber-physical systems are subject to cyberattacks and may be affected by faulty sensors. The consequent anomalous data can risk the safe operation of autonomous vehicles and may even lead to fatal crashes. Hence, in the third chapter, we develop an unsupervised/semi-supervised machine learning approach to address this gap. Specifically, this approach incorporates an additional autoencoder module into a generative adversarial network, which enables effective learning of the distribution of non-anomalous data. We term our approach GAN-enabled autoencoder for anomaly detection (GAAD). We evaluate the proposed approach using the Lyft Level 5 dataset and demonstrate its superior performance compared to state-of-the-art benchmarks. The prediction of a safe collision-free trajectory is probably the most important factor preventing the full adoption of autonomous vehicles in a public road. Despite recent advancements in motion prediction utilizing machine learning approaches for autonomous driving, the field is still in its early stages and necessitates further development of more effective methods to accurately estimate the future states of surrounding agents. Hence, in the fourth chapter, we introduce a novel deep learning approach for detecting the future trajectory of surrounding vehicles using a high-resolution semantic map and aerial imagery. Our proposed approach leverages integrated spatial and temporal learning to predict future motion. We assess the efficacy of our proposed approach on the Lyft Level 5 prediction dataset and achieve a comparable performance on various motion prediction metrics