In this paper, we propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation, a crucial component of many robotics and vision tasks such as navigation and virtual reality where relative camera poses are required in real time. We formulate this problem as optimizing a variational information bottleneck objective function, which eliminates pose-irrelevant information from the latent representation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language. Specifically, we bound the generalization errors of the deep information bottleneck framework and the predictability of the latent representation. These provide not only a performance guarantee but also practical guidance for model design, sample collection, and sensor selection. Furthermore, the stochastic latent representation provides a natural uncertainty measure without the needs for extra structures or computations. Experiments on two well-known odometry datasets demonstrate the effectiveness of our method.
翻译:在本文中,我们提出一个统一的以学习为动力的方法的信息理论框架,目的是进行odorization估计,这是许多机器人和视觉任务的重要组成部分,例如导航和虚拟现实,需要实时安装相对相机。我们将此问题表述为优化变异信息瓶颈目标功能,从潜在代表中消除与表面无关的信息。拟议框架为信息理论语言的业绩评估和理解提供了一个优雅的工具。具体地说,我们约束了深层信息瓶颈框架的概括错误和潜在代表的可预测性。这些不仅为模型设计、样本采集和传感器选择提供了绩效保障,而且还提供了实用指导。此外,随机潜在代表提供了一种自然的不确定性计量,而不需要额外的结构或计算。关于两个众所周知的odologicat数据集的实验显示了我们方法的有效性。