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Commit Graph

5 Commits (b5360b69b8b3f6d27441075f0955c462a92d89b5)

Author	SHA1	Message	Date
RUSHIL AMBARISH KADU	feaa8bd321	feat(shenron): Stabilize radar physics with spatial and signal calibration Includes -2m shift, Z-filtering, and Blackman-Harris windowing	2 months ago
RUSHIL AMBARISH KADU	9760c4c946	feat(shenron): Fix CARLA 0.9.16 semantic alignment and restore test-suite This includes the [NEW] intel/possible_issue_resolution.md file	2 months ago
RUSHIL AMBARISH KADU	831be75c51	feat(shenron): Physics calibration - Iteration 07 High-Def Sync	2 months ago
RUSHIL AMBARISH KADU	8ec04b881c	feat(shenron): Integrate C-SHENRON physics-based radar simulation - Transitions simulation to Model-Based Development (MBD) architecture. - Added semantic LiDAR sensor with high-fidelity point cloud recording. - Fixed '0 points' bug by implementing bit-casting for uint32 semantic tags (Tag 10 -> Metal). - Corrected antenna count hardcoding and range-slicing errors in radar processor. - Synchronized hardware configs between project settings and simulation engine. - Automated synthesis and MCAP serialization in src/main.py cleanup phase. - Verified rich 5-field point cloud output (x, y, z, velocity, magnitude) in showcase.	2 months ago
RUSHIL AMBARISH KADU	c568c11106	feat: Integrate C-SHENRON physics-based radar simulation pipeline Comprehensive integration of the Shenron radar model, enabling high-fidelity physics-driven radar synthesis from semantic LiDAR data. Core Changes: - Sensor Migration: Updated EGO vehicle to use 'sensor.lidar.ray_cast_semantic'. - Recorder Enhancement: Modified src/recorder.py to dynamically capture 6/7nd-column semantic LiDAR buffers [x, y, z, cos, obj, tag]. - Orchestration: Created scripts/ISOLATE/model_wrapper.py with the ShenronRadarModel class for a unified physics-to-detection API. - Processor Logic: Fixed broadcasting bugs and hardcoded antenna counts in RadarProcessor to support dynamic radar models (radarbook/ti_cascade). - Rich Data Output: Updated extraction logic to include [velocity, magnitude] parameters for ADAS performance analysis. - Batch Pipeline: Added scripts/generate_shenron.py to synthesize radar data as a post-processing step, maintaining simulation performance. - Visualization: Updated scripts/data_to_mcap.py to support the new /radar/shenron topic with a 5-field rich PointCloud schema. Stability & Fixes: - Implemented CUDA-enabled PyTorch checks with a robust CPU fallback. - Corrected CARLA 0.9.16 semantic column mapping in ISOLATE/lidar.py. - Handled indexing and range-slicing errors in the CFAR detection routine.	2 months ago