Project Overview

Design Overview

Project Demo

filler

The challenge lies in establishing industry-standard protocols for lidar sensor development, leading to difficulties in building a reliable object classification training model due to data inconsistencies, ultimately hindering cross-compatibility. Additionally, there is a need to create an open-source dataset for ISU to use as a reliable data source.

This senior design project aims to leverage LiDAR technology for an advanced environmental object classification system. The goal is to create a robust system that collects, processes, and builds a comprehensive database from LiDAR data. This database will serve as the foundation for training a deep learning-based classification model capable of identifying various objects in real-time, contributing to applications such as public safety, communication networks, education, urban planning, and resource management.

The expected deliverables include designing a LiDAR data collection system, developing algorithms for data preprocessing, creating a structured database, implementing feature extraction algorithms, training a deep learning classification model, labeling and validating data, and ultimately integrating the system into a real-time classification system. This comprehensive approach ensures the development of a powerful tool with wide-ranging applications.


Team Members

Ella Rekow

Team Lead

Sachin Patel

Deep Learning SME

Zachary Schmalz

Quality Assurance Lead

Anuraag Pujari

Data Architect

Ryan Sand

Data Collection Lead

Daniel Rosenhamer

Communications Manager

Weekly Reports

Report 1 (Sept 03 - Sept 17)
Report 2 (Sept 18 - Oct 09)
Report 3 (Sept 25 - Oct 08)
Report 4 (Oct 23 - Nov 05)
Report 5 (Nov 5 - Nov 19)
Report 6 (Jan 16 - Jan 28)
Report 7 (Jan 29 - Feb 11)
Report 8 (Feb 12 - Feb 24)




Design Documents

Design Document
Final Report



Additional deliverables

Final Presentation
Poster