1 code implementation • 22 May 2022 • Pasquale Antonante, Heath Nilsen, Luca Carlone
This paper investigates runtime monitoring of perception systems.
no code implementations • 11 Nov 2020 • Pasquale Antonante, David I. Spivak, Luca Carlone
The resulting temporal diagnostic graphs (i) provide a framework to reason over the consistency of perception outputs -- across modules and over time -- thus enabling fault detection, (ii) allow us to establish formal guarantees on the maximum number of faults that can be uniquely identified in a given perception system, and (iii) enable the design of efficient algorithms for fault identification.
no code implementations • 29 Jul 2020 • Pasquale Antonante, Vasileios Tzoumas, Heng Yang, Luca Carlone
We extend ADAPT and GNC to the case where the user does not have prior knowledge of the inlier-noise statistics (or the statistics may vary over time) and is unable to guess a reasonable threshold to separate inliers from outliers (as the one commonly used in RANSAC).
no code implementations • 24 May 2020 • Pasquale Antonante, David I. Spivak, Luca Carlone
Towards this goal, we draw connections with the literature on self-diagnosability for multiprocessor systems, and generalize it to (i) account for modules with heterogeneous outputs, and (ii) add a temporal dimension to the problem, which is crucial to model realistic perception systems where modules interact over time.
4 code implementations • 18 Sep 2019 • Heng Yang, Pasquale Antonante, Vasileios Tzoumas, Luca Carlone
In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case.
1 code implementation • 27 Mar 2019 • Vasileios Tzoumas, Pasquale Antonante, Luca Carlone
First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently.