Training End-to-End Steering of a Self-Balancing Mobile Robot Based on RGB-D Image and Deep ConvNet

Li, Chih-Hung G.; Long-ping, Zhou

doi:10.1109/aim43001.2020.9158810

Cited by 6 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2: Factory floor and cluttered room indoors [49] Factory floor 230 13 [34] Corridor indoors with few obstacles 340 7 [35] Corridor, kitchen and laboratory space 600 2 [36] Cluttered corridor indoors 7000 - [50] Cluttered maze-like indoor environment 4000 7 [51] Room with few obstacles 100 4 [52] Corridor indoors 400 55…”

Section: Approximate Sensors Cost (Gbp)mentioning

confidence: 99%

End-to-End Learning for Visual Navigation of Forest Environments

Niu

Zauner

Tarapore

2023

Forests

View full text Add to dashboard Cite

Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day.

show abstract