Flexible Trinocular: Non-rigid Multi-Camera-IMU Dense Reconstruction for UAV Navigation and Mapping

TL;DR

This paper introduces a visual-inertial system for UAVs that estimates non-rigid trinocular camera baselines for accurate, long-range depth mapping, integrating inertial data, photometric optimization, and probabilistic modeling.

Contribution

It presents a novel framework combining inertial measurements, photometric optimization, and probabilistic wing modeling within an EKF for non-rigid multi-camera depth estimation on UAVs.

Findings

Effective depth estimation in real-world UAV scenarios

Robustness under challenging environmental conditions

Computational efficiency suitable for onboard processing

Abstract

In this paper, we propose a visual-inertial framework able to efficiently estimate the camera poses of a non-rigid trinocular baseline for long-range depth estimation on-board a fast moving aerial platform. The estimation of the time-varying baseline is based on relative inertial measurements, a photometric relative pose optimizer, and a probabilistic wing model fused in an efficient Extended Kalman Filter (EKF) formulation. The estimated depth measurements can be integrated into a geo-referenced global map to render a reconstruction of the environment useful for local replanning algorithms. Based on extensive real-world experiments we describe the challenges and solutions for obtaining the probabilistic wing model, reliable relative inertial measurements, and vision-based relative pose updates and demonstrate the computational efficiency and robustness of the overall system under challenging conditions.