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<!DOCTYPE html>
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<meta name="description" content="TERA">
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<h1 class="title is-1 publication-title"><span style="color: rgb(1, 161, 27)">TERA</span>: Simulation Environment for Terrain Excavation Robot Autonomy</h1>
<!-- <h1 style="font-size:1.5rem">European Conference on Computer Vision (ECCV) 2024</h1> -->
<!-- <h1 style="font-size:1.5rem">ECCV 2024<br>(European Conference on Computer Vision)<br></h1> -->
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://christoaluckal.com" target="_blank">Christo Aluckal</a>,</span>
<span class="author-block">
<a href="" target="_blank">Roopesh Vinodh Kumar Lal</a>,</span>
<span class="author-block">
<a href="" target="_blank">Sean Courtney</a>,</span>
<span class="author-block">
<a href="https://yash.turkar.in/" target="_blank">Yash Turkar</a>,</span>
<span class="author-block">
<a href="https://yashomdighe.com/" target="_blank">Yashom Dighe</a>,</span>
<span class="author-block">
<a href="" target="_blank">Young-Jin Kim</a>,</span>
<span class="author-block">
<a target="_blank">Jake Gemerek</a>,</span>
<span class="author-block">
<a href="https://cse.buffalo.edu/faculty/kdantu/" target="_blank">Karthik Dantu</a></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Center for Embodied Autonomy and Robotics (CEAR) <br> University at Buffalo</span>
</div>
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<a href="https://www.buffalo.edu/">
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<h2 class="title is-3">Abstract</h2>
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<p>
Developing excavation autonomy is challenging given the environments where excavators operate, the complexity of physical interaction and the degrees of freedom of operation of the excavator itself. Simulation is a useful tool to build parts of the autonomy without the complexity of experimentation. Traditional excavator simulators are geared towards high fidelity interactions between the joints or between the terrain but do not incorporate other challenges such as perception required for end-end autonomy. A complete simulator should be capable of supporting real-time operation while providing high fidelity simulation of the excavator(s), the environment, and their interaction. In this paper we present TERA (Terrain Excavation Robot Autonomy), a simulator geared towards autonomous excavator applications based on Unity3D/AGX that provides the extensibility and scalability required to study full autonomy. It provides the ability to configure the excavator and the environment per the user requirements. We also demonstrate realistic dynamics by incorporating a time-varying model that introduces variations in the system’s responses. The simulator is then evaluated with different scenarios such as track deformation, velocities on different terrains, similarity of the system with the real excavator and the overall path error to show the capabilities of the simulation.
</p>
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<p>
MARs embeds attention information into height and width-disparate similarty spaces, driving the <strong><em>what</em></strong>
and <strong><em>where</em></strong> of multi-view attention focus together while avoiding the trivial solution (e.g., both
attention maps are zero everywhere).
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<div class="content has-text-justified">
<p>
To facilitate future research in the challenging space landmark recognition problem, we introduce the <strong><em>Luna-1</em></strong>
dataset, consisting of 5,067 Moon crater landmarks and 2,161 emulated orbital navigation frames from real-world
NASA data.
</p>
</div>
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<img style="width:49%; padding-right: 15px;" src="static/images/craters.png"/>
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<img style="width:100%;" src="static/images/Warehouse-Suppl2.png">
<h6>
Evaluation on Warehouse dataset (simulated); (a),(b),(c),(d) show point clouds ground truth, LeGO-LOAM, FAST-LIO2 and SHINE. (d) exhibits highest Qc,Qa,Qa as shown in zoomed-in view but contains artifacts (lower Qt compared to (c)). Empir3D accurately quantifies all aspects of quality while Dc and Dh identify (c) and (d) as the most similar
</h6>
<img style="width:100%;" src="static/images/suppl_learning_detailed.png">
<h6>
Evaluation on point cloud completion, Lamp from MVP reconstructed using PCN, TOPNET, ECG. ECG generates highest quality point clouds which is confirmed by Empir3D metrics while Dc and Dh identify PCN despite presence of artifacts and poor reconstruction.
</h6>
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<!-- Compute -->
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<p>
Comparative analysis of memory, CPU utilization and computation time across different region sizes (r) and number of processor cores. Memory utilization remains relatively constant across various core counts, whereas CPU utilization increases with more cores, especially for smaller region sizes before decreasing again when regions become too large for multi-threading. Computation time decreases significantly with the increase in region size up to a certain point, beyond which the benefits plateau, as shown in the benchmarks for 1, 2, 4, and 8 core computation of Empir3D, compared to the baseline single-threaded implementations Dc and Dh.
</p>
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<h6>
Attention alignment evolution during training for MARs (bottom row) against rotational equivarant and spatial attention layers (RIC CA, top row)
for Mars Crater (left), Moon Crater (middle), and Earth Stadium (right) features.
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<pre><code>@inproceedings{aluckal2024tera,
title={TERA: Simulation Environment for Terrain Excavation Robot Autonomy},
author={Christo Aluckal, Roopesh Vinodh Kumar Lal, Sean Courtney, Yash Turkar, Yashom Dighe, Young-Jin Kim, Jake Gemerek, Karthik Dantu},
year={2024}
}
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