Depth estimation on embedded computers for robot swarms in forest

TL;DR

This paper develops and evaluates low-cost depth estimation models for forest-floor robot swarms using embedded computers, demonstrating feasible accuracy and efficiency on Raspberry Pi 4 and Jetson Nano.

Contribution

It introduces two depth estimation models optimized for embedded hardware and evaluates their performance in forest environments, advancing low-cost autonomous navigation for robot swarms.

Findings

Auto-encoder network on Raspberry Pi 4 runs at 3.4 W power and 13 ms runtime.

Multi-scale deep network performs better with blurred RGB images.

Models trained on forest dataset show promising accuracy and efficiency.

Abstract

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.