added pipe-planner research post

Author: seungchan-kim

_bibliography/references.bib

@@ -16,6 +16,7 @@ @inproceedings{baek2025pipe
 	year         = {2025},
 	booktitle    = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 	url          = {https://arxiv.org/pdf/2503.07504},
+	video		 = {https://youtu.be/oZEqbCBRn-I?si=P-H09k57_BnxLHrL},
 	abstract     = {Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration.}
 }
 @inproceedings{patel2025tartanground,

_posts/2025-08-20-pipe.md

@@ -0,0 +1,23 @@
+---
+layout: post
+title: "PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration"
+date: 2025-08-20 2:00:01
+categories: research
+description: "We propose PIPE planner, an indoor exploration framework that computes pathwise information gain and uses map prediction to mitigate sensor coverage overestimation"
+author: "Seungchan Kim"
+published: true
+redirect: "https://pipe-planner.github.io/"
+show_sidebar: false
+# slim_content_width: true
+permalink: /pipe-planner/
+image: /img/posts/2025-08-20-pipe/pipe-intro.gif
+datatable: true
+title_image: None
+hero_image: /img/posts/2025-08-20-pipe/pipe-intro.gif
+hero_height: is-large
+remove_hero_title: false
+menubar_toc: false
+tags: Planning
+---
+
+We propose PIPE planner, an indoor exploration framework that computes pathwise information gain and uses map prediction to mitigate sensor coverage overestimation