This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (PJSO) problem. The work leads to remarkable driving performance improvements including more robust and precise collision avoidance, higher control feasibility, higher computation efficiency and stricter driving comfort guarantee, compared with other existing algorithms. The advantages of our algorithm are attributed to our fast iterative collision checks with exact vehicle/obstacle shapes, strict non-holonomic dynamic constraints and accurate kinematics-based speed optimization. It has been validated that, through batch simulation and road experiments, compared with prior works, our algorithm is with the highest robustness and capable to maintain the lowest failure rate ( $\sim\!\text{7}\%$ ) at nearly all test conditions, achieves 10x faster computational speed than other planners, fulfills $\text{100}\%$ driving-comfort standards in complex driving scenarios, and does not induce significant time increase as boundaries or obstacles scale up.

Autonomous Driving Trajectory Optimization With Dual-Loop Iterative Anchoring Path Smoothing and Piecewise-Jerk Speed Optimization

This work presents a survey on the usage of user-adaptive techniques for human interface with Social Robots, with focus on non-physical interaction. The work is based on an analysis of a number of recent scientific works, and aims to uncover existing scientific and technological gaps which can serve as basis for future research and development work. User-adaptive systems consist of autonomous agents that are able to use some manner of information on their user in order to adapt to them. Through their adaptive nature, these systems have been shown to be easier to accept by end-users, and to lead to improvements in a myriad of objective and subjective performance measurements. Thus, in the context of a growing domestic Social Robot industry, it becomes of key importance to study the scientific and technological frontiers of this field. In order to uncover potential lines of future research, we propose a taxonomy for the classification of works, which we use to analyse the works under survey, exposing the current scientific frontiers of the area. Aiming to establish the overall readiness of the field, we also analyse the maturity of the works under survey, exposing the current technological level of the techniques at hand and discussing a number of technological challenges.

User-Adaptive Interaction in Social Robots: A Survey Focusing on Non-physical Interaction

Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios. However, they commonly have a high latency due to the expensive data association and nonlinear optimization. This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We formulate the feature selection as a combinatorial optimization problem under a cardinality constraint to preserve the information matrix's spectral attributes. The stochastic-greedy algorithm is applied to approximate the optimal results in real-time. To avoid ill-conditioned estimation, we also propose a general strategy to evaluate the environment's degeneracy and modify the feature number online. The proposed feature selector is integrated into a multi-LiDAR SLAM system. We validate this enhanced system with extensive experiments covering various scenarios on two sensor setups and computation platforms. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems. To benefit the community, we have released the source code: https://ram-lab.com/file/site/m-loam.

pdf/greedy-based-feature-selection-for-efficient-lidar-slam-5ai8srk1o3.pdf

Greedy-Based Feature Selection for Efficient LiDAR SLAM

Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.

/pdf/a-survey-on-visual-navigation-and-positioning-for-autonomous-2kwsrem9.pdf

A Survey on Visual Navigation and Positioning for Autonomous UUVs

Telepresence robots are becoming popular in social interactions involving health care, elderly assistance, guidance, or office meetings. There are two types of human psychological experiences to consider in robot-mediated interactions: (1) telepresence, in which a user develops a sense of being present near the remote interlocutor, and (2) co-presence, in which a user perceives the other person as being present locally with him or her. This work presents a literature review on developments supporting robotic social interactions, contributing to improving the sense of presence and co-presence via robot mediation. This survey aims to define social presence, co-presence, identify autonomous “user-adaptive systems” for social robots, and propose a taxonomy for “co-presence” mechanisms. It presents an overview of social robotics systems, applications areas, and technical methods and provides directions for telepresence and co-presence robot design given the actual and future challenges. Finally, we suggest evaluation guidelines for these systems, having as reference face-to-face interaction.

/pdf/telepresence-social-robotics-towards-co-presence-a-review-2bgnm4wo.pdf

Telepresence Social Robotics towards Co-Presence: A Review

The TEB hierarchical planner for real-time navigation through unknown environments is highly effective at balancing collision avoidance with goal directed motion. Designed over several years and publications, it implements a multi-trajectory optimization based synthesis method for identifying topologically distinct trajectory candidates through navigable space. Unfortunately, the underlying factor graph approach to the optimization problem induces a mismatch between grid-based representations and the optimization graph, which leads to several time and optimization inefficiencies. This paper explores the impact of using egocentric, perception space representations for the local planning map. Doing so alleviates many of the identified issues related to TEB and leads to a new method called egoTEB. Timing experiments and Monte Carlo evaluations in benchmark worlds quantify the benefits of egoTEB for navigation through uncertain environments.

egoTEB: Egocentric, Perception Space Navigation Using Timed-Elastic-Bands

Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visual-inertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory using the feedback from visual-inertial estimation. By extensively evaluating the trajectory tracking performance of representative state-of-the-art visual-inertial SLAM systems, we reveal the importance of latency reduction in visual estimation module of these systems. The findings suggest directions of future improvements for visual-inertial SLAM.

Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy

The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical characteristic of resource-limited applications. While hardware and algorithm advances have been significantly improved the cost-efficiency of VSLAM front-ends, the cost-efficiency of VSLAM back-ends remains a bottleneck. This paper describes a novel, rigorous method to improve the cost-efficiency of local BA in a BA-based VSLAM back-end. An efficient algorithm, called Good Graph, is developed to select size-reduced graphs optimized in local BA with condition preservation. To better suit BA-based VSLAM back-ends, the Good Graph predicts future estimation needs, dynamically assigns an appropriate size budget, and selects a condition-maximized subgraph for BA estimation. Evaluations are conducted on two scenarios: 1) VSLAM as standalone process, and 2) VSLAM as part of closed-loop navigation system. Results from the first scenario show Good Graph improves accuracy and robustness of VSLAM estimation, when computational limits exist. Results from the second scenario, indicate that Good Graph benefits the trajectory tracking performance of VSLAM-based closed-loop navigation systems, which is a primary application of VSLAM.

Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM

Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives. The resulting visual navigation strategies work very well at collision avoidance and have performance that matches traditional reactive navigation algorithms while operating in real-time. It is accepted that these solutions cannot provide the same level of performance as a global planner. However, it is less clear how such end-to-end systems should be integrated into a full navigation pipeline. We evaluate the typical end-to-end solution within a full navigation pipeline in order to expose its weaknesses. Doing so illuminates how to better integrate deep learning methods into the navigation pipeline. In particular, we show that they are an efficient means to provide informed samples for sample-based planners. Controlled simulations with comparison against traditional planners show that the number of samples can be reduced by an order of magnitude while preserving navigation performance. Implementation on a mobile robot matches the simulated performance outcomes.

Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation

We give an overview of how the CADENCE architecture addresses the problem of turn-taking in embodied interaction.

CADENCE for Collaboration and Companionship with Robots.

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