# CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization

arXiv: https://arxiv.org/abs/2309.05200 · HTML: https://mlsanity.com/paper/2309.05200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping](https://mlsanity.com/paper/1909.04631/) (2019)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping](https://mlsanity.com/paper/1905.02238/) (2019)
- [Confidence-rich grid mapping](https://mlsanity.com/paper/2006.15754/) (2020)
- [Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference](https://mlsanity.com/paper/2106.14986/) (2021)
- [Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring](https://mlsanity.com/paper/1607.01883/) (2016)
- [Occupancy Map Building through Bayesian Exploration](https://mlsanity.com/paper/1703.00227/) (2017)
- [Bayesian Optimisation for Safe Navigation under Localisation Uncertainty](https://mlsanity.com/paper/1709.02169/) (2017)
- [PROBE-GK: Predictive Robust Estimation using Generalized Kernels](https://mlsanity.com/paper/1708.00171/) (2017)
- [Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration](https://mlsanity.com/paper/2202.09631/) (2022)

## Abstract (arXiv)

In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments. We also provide our open-source implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.