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RoboticsIn Development
Data · Validation · Maps
LiDAR Mapping Pipeline
An end-to-end pipeline that captures robotics sensor streams, validates their integrity, and processes them into usable infrastructure maps. It emphasizes data quality, repeatability and visual inspection of point clouds before downstream mapping.
Status
In Development
Timeline
2026
Role
Developer
Category
Robotics
Technology Stack
ROS BagRVizVelodyne LiDARTFPoint Cloud LibraryDockerLinux
Problem
Raw robotics captures are easy to corrupt and hard to trust. Downstream mapping fails silently when input data has gaps, bad timestamps or broken transforms.
Constraints
- Large sensor datasets
- Strict timing/TF correctness
- Reproducible runs across machines
Solution
A structured record → validate → process workflow with automated integrity checks and visual QA in RViz before map generation.
Key Decisions
- Validation as a first-class stage
- Containerized, deterministic processing
System Architecture
01Record (ROS Bag)
02Validate (timing, TF, coverage)
03Process (registration & filtering)
04Map generation
05Point-cloud QA
Diagram placeholder — replace with a detailed architecture diagram when available.
Hardware
- Velodyne LiDAR
- Robotics compute
Software
- ROS Bag tooling
- Point Cloud Library
- RViz
- Docker
Challenges
- Handling large datasets efficiently
- Detecting subtle data corruption early
Results
- Actively developed — sample maps and QA tooling will be documented here.
Lessons Learned
- Validating input early saves enormous debugging downstream.
Media
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