This article tackles the problem of designing 3D perception systems for robots with high visual requirements, such as versatile legged robots capable of different locomotion styles. In order to guarantee high visual coverage in varied conditions (e.g., biped walking, quadruped walking, ladder climbing), such robots need to be equipped with a large number of sensors, while at the same time managing the computational requirements that arise from such a system. We tackle this problem at both levels: sensor placement (how many sensors to install on the robot and where) and run-time acquisition scheduling under computational constraints (not all sensors can be acquired and processed at the same time). Our first contribution is a methodology for designing perception systems with a large number of depth sensors scattered throughout the links of a robot, using multi-objective optimization for optimal trade-offs between visual coverage and the number of sensors. We estimate the Pareto front of these objectives through evolutionary optimization, and implement a solution on a real legged robot. Our formulation includes constraints on task-specific coverage and design symmetry, which lead to reliable coverage and fast convergence of the optimization problem. Our second contribution is an algorithm for lowering the computational burden of mapping with such a high number of sensors, formulated as an information-maximization problem with several sampling techniques for speed. Our final system uses 20 depth sensors scattered throughout the robot, which can either be acquired simultaneously or optimally scheduled for low CPU usage while maximizing mapping quality. We show that, when compared with state-of-the-art robotic platforms, our system has higher coverage across a higher number of tasks, thus being suitable for challenging environments and versatile robots. We also demonstrate that our scheduling algorithm allows higher mapping performance to be obtained than with naïve and state-of-the-art methods by leveraging on measures of information gain and self-occlusion at low computational costs.

中文翻译：

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放置和调度多个深度传感器，以在多功能腿式机器人中实现广泛覆盖和高效映射

本文解决了为具有高视觉要求的机器人设计 3D 感知系统的问题，例如能够进行不同运动方式的多功能腿式机器人。为了保证在各种条件下（例如，双足步行、四足步行、爬梯）的高视觉覆盖率，此类机器人需要配备大量传感器，同时管理由此产生的计算需求。系统。我们在两个层面解决这个问题：传感器放置（在机器人上安装多少个传感器以及在哪里安装）和计算约束下的运行时采集调度（并非所有传感器都可以同时采集和处理）。我们的第一个贡献是一种设计感知系统的方法，其中大量深度传感器分散在机器人的各个链接中，使用多目标优化在视觉覆盖范围和传感器数量之间进行最佳权衡。我们通过进化优化来估计这些目标的帕累托前沿，并在真正的腿式机器人上实施解决方案。我们的公式包括对特定任务覆盖和设计对称性的约束，这导致优化问题的可靠覆盖和快速收敛。我们的第二个贡献是一种算法，用于降低使用如此大量传感器进行映射的计算负担，将其表述为具有多种采样技术以提高速度的信息最大化问题。我们的最终系统使用分散在整个机器人中的 20 个深度传感器，它们可以同时获取，也可以优化调度以降低 CPU 使用率，同时最大限度地提高映射质量。我们表明，与最先进的机器人平台相比，我们的系统在更多任务上具有更高的覆盖范围，因此适用于具有挑战性的环境和多功能机器人。我们还证明，我们的调度算法通过在低计算成本下利用信息增益和自遮挡的措施，可以获得比使用朴素和最先进的方法更高的映射性能。