Ruchita Vehale

Designed and deployed image segmentation pipelines to precisely isolate defect regions across complex vehicle surfaces, improving detection accuracy and repeatability in production.

Built a Blender plugin automating 3D model processing workflows. A 2-day manual task compressed to under 1 hour (16× improvement), deployed in live inspection pipelines.

Implemented 3D model + 2D image hybrid fusion for pose estimation, significantly reducing reliance on expensive LiDAR hardware while maintaining accuracy across inspection stations.

Developed calibration and localisation algorithms across multiple inspection pipelines, reducing manual intervention and enabling scalable deployment.

Improved codebase architecture and version control workflows, enabling faster, more reliable production deployments across the R&D team.

Designed and implemented a reverse landing system using GPS + Raspberry Pi camera fusion, enabling precise autonomous landings in dynamic conditions.

Developed stereo camera-based depth estimation to replace monocular approaches, providing sub-metre altitude control for low-altitude navigation.

Built LiDAR + camera sensor fusion pipeline for robust drone detection and accurate landing zone identification in cluttered environments.

Integrated MAVLink protocol and Wi-Fi communication for seamless ground-to-drone control and real-time telemetry transmission.

Contributed to coaxial drone R&D: assembly, control system tuning, and performance testing across varied flight conditions for synchronised display applications.

Developed a precision autonomous landing system using GPS and camera data fusion for reliable operation in dynamic, GPS-noisy environments.

Optimised antenna designs using CST Microwave Studio, achieving 20% size reduction and 70% accuracy improvement for GPS, Wi-Fi, Bluetooth and 5G frequencies.

Operated 3D printers (FDM, Inkjet) and characterisation tools (XRD, VNA) for antenna prototyping and dielectric material testing.

Fabricated a biodegradable-ink strain sensor via 3D inkjet printing - a novel approach to sustainable flexible electronics with defence and wearable applications.

Research led to 3 journal publications in Wiley and RSC journals, including novel dielectric nanocomposite materials for microwave applications.

Designed and deployed a MATLAB-based video tracking system using Gaussian Mixture Model and point tracking for moving object recognition in defence research contexts.

Achieved robust accuracy under variable lighting conditions through adaptive background subtraction and GMM model tuning.

Executed feature extraction and depth estimation using projective geometry across multiple camera planes for accurate 3D reconstruction.

Validated precise velocity measurement across 20 independent video datasets, with reliable real-world performance.