# PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief Propagation

arXiv: https://arxiv.org/abs/2406.09726 · HTML: https://mlsanity.com/paper/2406.09726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Dissecting the Graphcore IPU Architecture via Microbenchmarking](https://mlsanity.com/paper/1912.03413/) (2019)
- [Pano3D: A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation](https://mlsanity.com/paper/2109.02749/) (2021)
- [Bundle Adjustment on a Graph Processor](https://mlsanity.com/paper/2003.03134/) (2020)
- [A visual introduction to Gaussian Belief Propagation](https://mlsanity.com/paper/2107.02308/) (2021)
- [Dimensions of Motion: Monocular Prediction through Flow Subspaces](https://mlsanity.com/paper/2112.01502/) (2021)
- [A Robot Web for Distributed Many-Device Localisation](https://mlsanity.com/paper/2202.03314/) (2022)
- [A Survey of Distributed Optimization Methods for Multi-Robot Systems](https://mlsanity.com/paper/2103.12840/) (2021)
- [FPGA-based ORB Feature Extraction for Real-Time Visual SLAM](https://mlsanity.com/paper/1710.07312/) (2017)
- [BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal Plane](https://mlsanity.com/paper/2004.11186/) (2020)
- [Distributed Simultaneous Localisation and Auto-Calibration using Gaussian Belief Propagation](https://mlsanity.com/paper/2401.15036/) (2024)
- [PI-BA Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization](https://mlsanity.com/paper/1905.02373/) (2019)
- [ORB-based SLAM accelerator on SoC FPGA](https://mlsanity.com/paper/2207.08405/) (2022)

## Abstract (arXiv)

Images are the standard input for most computer vision algorithms. However, their processing often reduces to parallelizable operations applied locally and independently to individual pixels. Yet, many of these low-level raw pixel readings only provide redundant or noisy information for specific high-level tasks, leading to inefficiencies in both energy consumption during their transmission off-sensor and computational resources in their subsequent processing. As novel sensors featuring advanced in-pixel processing capabilities emerge, we envision a paradigm shift toward performing increasingly complex visual processing directly in-pixel, reducing computational overhead downstream. We advocate for synthesizing high-level cues at the pixel level, enabling their off-sensor transmission to directly support downstream tasks more effectively than raw pixel readings. This paper conceptualizes a novel photometric rotation estimation algorithm to be distributed at pixel level, where each pixel estimates the global motion of the camera by exchanging information with other pixels to achieve global consensus. We employ a probabilistic formulation and leverage Gaussian Belief Propagation (GBP) for decentralized inference using messaging-passing. The proposed proposed technique is evaluated on real-world public datasets and we offer a in-depth analysis of the practicality of applying GBP to distributed rotation estimation at pixel level.