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

Abstract

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.

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

“…Lightweight robots can cause less topsoil damage even with repeated wheeling, as shown by Calleja-Huerta et al [347]; therefore, the use of multiple lighter autonomous machines operating as a coordinated team instead of one big and heavier machine can help reduce soil compaction and damage [348], as suggested by Tarapore et al [341], who propose the use of what they term "sparse swarms" in forestry robotics. However, although some work has been conducted in preparation for this type of solution (e.g., [190,349]), no systematic work has been published that we are aware of. In summary, multi-robot cooperation in forestry can lead to more efficient and sustainable operations, reducing soil damage, improving overall forest health and protecting vegetation that should be preserved in the interventions sought with typical forestry tasks (see discussion in Section 5.1.4).…”

“…Autonomy is particularly critical in forestry applications-much more than in agriculture, in fact-and as such, requires specific strategies to be put in place. For example, Niu et al [190,349] used a Raspberry Pi, chosen due to its compact size, low cost, and low power requirements, for the deployment of their depth prediction model (see Section 4.1.2) and navigation algorithm, which are executed successfully in real time.…”