Perception algorithms that provide estimates of their uncertainty are crucial to the development of autonomous robots that can operate in challenging and uncontrolled environments. Such perception algorithms provide the means for having risk-aware robots that reason about the probability of successfully completing a task when planning. There exist perception algorithms that come with models of their uncertainty; however, these models are often developed with assumptions, such as perfect data associations, that do not hold in the real world. Hence the resultant estimated uncertainty is a weak lower bound. To tackle this problem we present introspective perception - a novel approach for predicting accurate estimates of the uncertainty of perception algorithms deployed on mobile robots. By exploiting sensing redundancy and consistency constraints naturally present in the data collected by a mobile robot, introspective perception learns an empirical model of the error distribution of perception algorithms in the deployment environment and in an autonomously supervised manner. In this paper, we present the general theory of introspective perception and demonstrate successful implementations for two different perception tasks. We provide empirical results on challenging real-robot data for introspective stereo depth estimation and introspective visual simultaneous localization and mapping and show that they learn to predict their uncertainty with high accuracy and leverage this information to significantly reduce state estimation errors for an autonomous mobile robot.

翻译：具备不确定性估计能力的感知算法对于在充满挑战且不受控环境中运行的自主机器人开发至关重要。这类感知算法为风险感知型机器人提供了在规划时推理任务成功完成概率的手段。现有一些感知算法虽内置不确定性模型，但这些模型通常基于完美数据关联等假设开发，这些假设在现实世界中并不成立，因此所估计的不确定性仅为弱下界。为解决此问题，我们提出内省式感知——一种用于准确预测移动机器人所部署感知算法不确定性估计的新方法。通过利用移动机器人采集数据中自然存在的感知冗余与一致性约束，内省式感知能够以自主监督方式在部署环境中学习感知算法误差分布的经验模型。本文阐述了内省式感知的通用理论，并展示了其在两种不同感知任务中的成功实现。我们基于具有挑战性的真实机器人数据，为内省式立体深度估计与内省式视觉同步定位与建图提供了实证结果，证明这些方法能够高精度地预测自身不确定性，并利用此信息显著降低自主移动机器人的状态估计误差。