  Robot swarms to date are not prepared for autonomous navigation such as path
planning and obstacle detection in forest floor, unable to achieve low-cost.
The development of depth sensing and embedded computing hardware paves the way
for swarm of terrestrial robots. The goal of this research is to improve this
situation by developing low cost vision system for small ground robots to
rapidly perceive terrain. We develop two depth estimation models and evaluate
their performance on Raspberry Pi 4 and Jetson Nano in terms of accuracy,
runtime and model size of depth estimation models, as well as memory
consumption, power draw, temperature, and cost of above two embedded on-board
computers. Our research demonstrated that auto-encoder network deployed on
Raspberry Pi 4 runs at a power consumption of 3.4 W, memory consumption of
about 200 MB, and mean runtime of 13 ms. This can be to meet our requirement
for low-cost swarm of robots. Moreover, our analysis also indicated multi-scale
deep network performs better for predicting depth map from blurred RGB images
caused by camera motion. This paper mainly describes depth estimation models
trained on our own dataset recorded in forest, and their performance on
embedded on-board computers.
