# Detection, localisation and tracking of pallets using machine learning techniques and 2D range data

arXiv: https://arxiv.org/abs/1803.11254 · HTML: https://mlsanity.com/paper/1803.11254/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 10 of 10)

- [Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing](https://mlsanity.com/paper/2304.06009/) (2023)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [Pallet Detection from Synthetic Data Using Game Engines](https://mlsanity.com/paper/2304.03602/) (2023)
- [Improving Pallet Detection Using Synthetic Data](https://mlsanity.com/paper/2402.07098/) (2024)
- [Localization of Pallets on Shelves Using Horizontal Plane Projection of a 360-degree Image](https://mlsanity.com/paper/2404.17118/) (2024)
- [Autonomous Navigation in Dynamic Environments: Deep Learning-Based Approach](https://mlsanity.com/paper/2102.08758/) (2021)
- [Pallet Detection And Localisation From Synthetic Data](https://mlsanity.com/paper/2503.22965/) (2025)
- [Lang2Lift: A Language-Guided Autonomous Forklift System for Outdoor Industrial Pallet Handling](https://mlsanity.com/paper/2508.15427/) (2025)
- [Learning-Based Vision Systems for Semi-Autonomous Forklift Operation in Industrial Warehouse Environments](https://mlsanity.com/paper/2511.06295/) (2025)
- [A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2609.22974/) (2026)

## Abstract (arXiv)

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labeled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.