Search Results for author: Joseph Ortiz

Found 8 papers, 4 papers with code

Theseus: A Library for Differentiable Nonlinear Optimization

1 code implementation19 Jul 2022 Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.

A Robot Web for Distributed Many-Device Localisation

no code implementations7 Feb 2022 Riku Murai, Joseph Ortiz, Sajad Saeedi, Paul H. J. Kelly, Andrew J. Davison

We show that a distributed network of robots or other devices which make measurements of each other can collaborate to globally localise via efficient ad-hoc peer to peer communication.

Incremental Abstraction in Distributed Probabilistic SLAM Graphs

no code implementations13 Sep 2021 Joseph Ortiz, Talfan Evans, Edgar Sucar, Andrew J. Davison

Scene graphs represent the key components of a scene in a compact and semantically rich way, but are difficult to build during incremental SLAM operation because of the challenges of robustly identifying abstract scene elements and optimising continually changing, complex graphs.

A visual introduction to Gaussian Belief Propagation

no code implementations5 Jul 2021 Joseph Ortiz, Talfan Evans, Andrew J. Davison

In this article, we present a visual introduction to Gaussian Belief Propagation (GBP), an approximate probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs.

iMAP: Implicit Mapping and Positioning in Real-Time

2 code implementations ICCV 2021 Edgar Sucar, Shikun Liu, Joseph Ortiz, Andrew J. Davison

We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera.

Bundle Adjustment on a Graph Processor

1 code implementation CVPR 2020 Joseph Ortiz, Mark Pupilli, Stefan Leutenegger, Andrew J. Davison

Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance for message passing algorithms on arbitrary graphs.

FutureMapping 2: Gaussian Belief Propagation for Spatial AI

no code implementations30 Oct 2019 Andrew J. Davison, Joseph Ortiz

We argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products.

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