An embarrassingly simple approach for visual navigation of forest environments

Abstract

Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

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

“…Workaround solutions have been, at times, used to circumvent the lack of depth information in camera images. For instance, recently, Niu et al [190] proposed a low-cost navigation system tailored for small-sized forest rovers, including a lightweight CNN, to estimate depth images from RGB input images from a low-viewpoint monocular camera. The depth prediction model proposed in this research is designed to infer depth information from low-resolution RGB images using an autoencoder architecture.…”

“…Examples of research in applying transfer learning to the more specific context of precision forestry applications, to our knowledge, are relatively few. Niu et al [190] employed transfer learning to train their model (see Section 4.1.2) on both indoor and outdoor datasets, including the real-world low-viewpoint forest dataset collected by the authors, described in Section 4.1.5. Andrada et al [157] showed that transfer learning techniques, such as weight initialization with class imbalance (with the "Humans" and "Animals" as the underrepresented classes; see the previous section, Figure 9) and pretrained weights, improved the overall quality of semantic segmentation.…”

“…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.…”

Focusing on Object Extremities for Tree Instance Segmentation in Forest Environments

“…Where Wang et al [11] and Fortin et al [5] will focus on detecting only the trunk of the tree, Russell [8] will identify tree tops in aerial views. Viewpoints also differ according to the robots used for application: drones [12] or Unmanned Ground Vehicles (UGV) [13], [14], to name but a few. The diversity of approaches is also reflected in the sensors used.…”