# Towards Long-term Robotics in the Wild

Source: [Towards Long-term Robotics in the Wild](<https://www.sota2.com/research/paper/68fa73071e209131cea59e91>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:39:34.909Z

Authors: Stephen Hausler, Ethan Griffiths, Milad Ramezani, Peyman Moghadam

Published: 2024-04-29T07:30:18.000Z

## Abstract

In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets exist for urban and suburban settings, those tailored to natural environments are scarce. Our recent benchmarks WildPlaces and WildScenes address this gap by providing synchronised image, lidar, semantic and accurate 6-DoF pose information in forest-type environments. We highlight the multi-modal nature of this dataset and discuss and demonstrate its utility in various downstream tasks, such as place recognition and 2D and 3D semantic segmentation tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.18477>)

[PDF](<https://arxiv.org/pdf/2404.18477.pdf>)