# Depth map estimation methodology for detecting free-obstacle navigation areas

## TL;DR

This paper introduces a vision-based method combining stereo camera depth maps and 1D LiDAR data, fused via Kalman filtering, to detect free obstacle areas for quadrotor navigation, implemented on an embedded system.

## Contribution

It presents a novel fusion approach of stereo vision and LiDAR data using Kalman filtering for obstacle detection in quadrotor navigation.

## Key findings

- Effective detection of free navigation areas demonstrated
- Fusion approach improves obstacle detection accuracy
- Implemented successfully on embedded hardware

## Abstract

This paper presents a vision-based methodology which makes use of a stereo camera rig and a one dimension LiDAR to estimate free obstacle areas for quadrotor navigation. The presented approach fuses information provided by a depth map from a stereo camera rig, and the sensing distance of the 1D-LiDAR. Once the depth map is filtered with a Weighted Least Squares filter (WLS), the information is fused through a Kalman filter algorithm. To determine if there is a free space large enough for the quadrotor to pass through, our approach marks an area inside the disparity map by using the Kalman Filter output information. The whole process is implemented in an embedded computer Jetson TX2 and coded in the Robotic Operating System (ROS). Experiments demonstrate the effectiveness of our approach.