6 accepted papers
Perception system performance can vary dramatically with contextual factors such as environmental geometry, appearance, and other phenomena. In this work we present a theoretical framework for understanding the role of context in perception and discuss three approaches for predicting probabilistic p…
Many modern perception systems require human engineers to tune parameters in order to adapt to various environments and applications. This incurs a large startup cost when deploying a robotic system by relying on human expertise and ground truth instrumentation. To alleviate this, we propose a techn
Modern perception systems are notoriously complex, featuring dozens of interacting parameters that must be tuned to achieve good performance. Conventional tuning approaches require expensive ground truth, while heuristic methods are difficult to generalize. In this work, we propose an introspective…
The popularity of cameras for perception is enabled in part by powerful intrinsic calibration routines, commonly requiring a user to manually collect images of a known calibration target. The manual nature of this process produces training data that is unevenly spread in the camera relative pose spa
Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly l…
The ubiquitous additive Gaussian noise model is favored in statistical modeling applications for its flexibility and ease of use. Often noise is assumed to be well-represented by a constant covariance, while in reality error characteristics may change predictably. We present an efficient parametric…