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Hongjin Chen

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2 papers

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ICRA Conference 2024 Conference Paper

Contrastive Learning-Based Attribute Extraction Method for Enhanced Terrain Classification

Xiao Liu
Hongjin Chen
Haoyao Chen

The outdoor environment has many uneven surfaces that put the robot at risk of sinking or tipping over. Recognizing the type of terrain can help robot avoid risks and choose an appropriate gait. One of the critical problems is how to extract the terrain-related knowledge from sensor data collected as the robot traversed the ground. Many existing vision-based approaches are limited in directly perceiving the intrinsic properties of various terrains. The intuitive approach entails directly analyzing data recorded by the robot’s proprioceptive sensors. However, it faces challenges in being specific to certain robot leg configurations or in the lack of interpretability of the extracted features. In this paper, a terrain attribute extraction algorithm is proposed based on contrastive learning. It leverages the haptic data generated from the interaction between the robot’s legs and terrain to automatically extract terrain attributes. The results demonstrate that the attributes extracted using this method strongly correlate with the actual softness of the terrain. Furthermore, these attributes played an important role in achieving high accuracy in terrain classification tasks.

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IROS Conference 2022 Conference Paper

Fast and Safe Exploration via Adaptive Semantic Perception in Outdoor Environments

Zhihao Wang 0003
Lingxu Chen
Hongjin Chen
Haoyao Chen
Xin Jiang

Autonomous exploration in unknown environments is a fundamental task for robots. Existing approaches mostly were concentrated on the efficiency of the exploration with the assumption of perfect state estimation, but the drift of pose estimation in visual SLAM occurs frequently and is detrimental to robot's localization and exploration performance. In this paper, a perception-aware exploration(PAE) method is proposed for rapidly and safely autonomous exploration in outdoor environments. The adaptive semantic information is proposed to improve the robustness of perception. Based on the perception module, both the selection of exploration goal on a novel weighted information gain and path planning can avoid the areas with high localization uncertainty. In addition, thanks to the proposed pipeline, including scan-based frontier detection, kd-tree based map prediction and suboptimal frontier buffer strategy, the PAE planner can explore the environment with high success rate and high efficiency. Several simulations are performed to verify the effectiveness of our methods.

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