# Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

arXiv: https://arxiv.org/abs/2112.02843 · HTML: https://mlsanity.com/paper/2112.02843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Data-Efficient Decentralized Visual SLAM](https://mlsanity.com/paper/1710.05772/) (2017)
- [A Resource-Aware Approach to Collaborative Loop Closure Detection with Provable Performance Guarantees](https://mlsanity.com/paper/1907.04904/) (2019)
- [Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling](https://mlsanity.com/paper/1505.05908/) (2015)
- [Near-Optimal Sensor Scheduling for Batch State Estimation: Complexity, Algorithms, and Limits](https://mlsanity.com/paper/1608.07533/) (2016)
- [Scheduling Nonlinear Sensors for Stochastic Process Estimation](https://mlsanity.com/paper/1609.08536/) (2016)
- [Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements](https://mlsanity.com/paper/1804.04317/) (2018)
- [Cooperative Localization under Limited Connectivity](https://mlsanity.com/paper/1904.13074/) (2019)
- [Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions](https://mlsanity.com/paper/1912.04709/) (2019)

## Abstract (arXiv)

In cooperative localization, communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling enables an agent to decide which subset of available inter-agent relative measurements it should process when its computational resources are limited. Optimal measurement scheduling is an NP-hard combinatorial optimization problem. The so-called sequential greedy (SG) algorithm is a popular suboptimal polynomial-time solution for this problem. However, the merit function evaluation for the SG algorithms requires access to the state estimate vector and error covariance matrix of all the landmark agents (teammates that an agent can take measurements from). This paper proposes a measurement scheduling for CL that follows the SG approach but reduces the communication and computation cost by using a neural network-based surrogate model as a proxy for the SG algorithm's merit function. The significance of this model is that it is driven by local information and only a scalar metadata from the landmark agents. This solution addresses the time and memory complexity issues of running the SG algorithm in three ways: (a) reducing the inter-agent communication message size, (b) decreasing the complexity of function evaluations by using a simpler surrogate (proxy) function, (c) reducing the required memory size.Simulations demonstrate our results.