A Go application that synchronously records multi-modal sensor data:
- Video from RTSP streams (via ffmpeg) — multiple cameras
- Audio from RTSP streams (via ffmpeg)
- Lidar point clouds from Zenoh topics
- Depth frames from Zenoh topics (RVL-compressed, lossless)
- Odometry messages from Zenoh topics
All streams are timestamped and organized into session directories for easy alignment and analysis.
- Go 1.25 or later
- ffmpeg installed and available in PATH
- zenoh-c library (automatically downloaded via
make download-zenohc)
Configure via environment variables:
ENABLE_COLLECTION- Enable/disable data collection (default:true; set tofalse,0, ornoto disable)TOP_CAMERA_RTSP_URL- Top camera stream URL (default:rtsp://localhost:8554/top_camera_raw)FRONT_CAMERA_RTSP_URL- Front camera stream URL (default:rtsp://localhost:8554/front_camera)DOWN_CAMERA_RTSP_URL- Down camera stream URL (default:rtsp://localhost:8554/down_camera)AUDIO_RTSP_URL- Audio stream URL (default:rtsp://localhost:8554/audio)LIDAR_ZENOH_ENDPOINT- Zenoh endpoint for lidar (default:tcp/127.0.0.1:7447)LIDAR_ZENOH_TOPIC- Zenoh topic for lidar data (default:scan)DEPTH_ZENOH_ENDPOINT- Zenoh endpoint for depth (default:tcp/127.0.0.1:7447)DEPTH_ZENOH_TOPIC- Zenoh topic for depth frames (default:camera/realsense2_camera_node/depth/image_rect_raw)ODOM_ZENOH_ENDPOINT- Zenoh endpoint for odometry (default:tcp/127.0.0.1:7447)ODOM_ZENOH_TOPIC- Zenoh topic for odometry data (default:odom)RECORDINGS_DIR- Base directory for recordings (default:recordings)
Download the zenoh-c library and build the binary:
make download-zenohc
make buildThe binary will be created at bin/om1-telemetry.
./bin/om1-telemetryOr with custom settings:
ENABLE_COLLECTION=true \
TOP_CAMERA_RTSP_URL="rtsp://camera.local/top_camera_raw" \
FRONT_CAMERA_RTSP_URL="rtsp://camera.local/front_camera" \
DOWN_CAMERA_RTSP_URL="rtsp://camera.local/down_camera" \
AUDIO_RTSP_URL="rtsp://camera.local/audio" \
LIDAR_ZENOH_ENDPOINT="tcp/192.168.1.10:7447" \
LIDAR_ZENOH_TOPIC="scan" \
DEPTH_ZENOH_ENDPOINT="tcp/192.168.1.10:7447" \
DEPTH_ZENOH_TOPIC="camera/realsense2_camera_node/depth/image_rect_raw" \
ODOM_ZENOH_ENDPOINT="tcp/192.168.1.10:7447" \
ODOM_ZENOH_TOPIC="odom" \
RECORDINGS_DIR="/path/to/recordings" \
./bin/om1-telemetryEach recording session creates a timestamped directory structure:
recordings/
└── 2026-05-15/
└── 2026-05-15_14-30-00/
├── meta.json # Session metadata
├── top_camera.mp4 # Top camera recording
├── front_camera.mp4 # Front camera recording
├── down_camera.mp4 # Down camera recording
├── audio.ogg # Audio recording
├── lidar_scans.bin # Raw lidar point cloud data
├── lidar_timestamps.csv # Timestamps: unix_ns,seq,byte_offset
├── depth_frames.bin # RVL-compressed depth frames (lossless)
├── depth_timestamps.csv # unix_ns,seq,byte_offset,byte_length,method,width,height,encoding
├── odom_frames.bin # Raw odometry messages
└── odom_timestamps.csv # Timestamps: unix_ns,seq,byte_offset
Each row in depth_timestamps.csv slices one frame out of depth_frames.bin
using byte_offset and byte_length. Depth is compressed with RVL
(Wilson, Fast Lossless Depth Image Compression, ISS 2017) — a lossless codec
designed for 16-bit depth maps, so depth values are preserved exactly while
typically using far less space than the raw frames.
The extra columns describe each frame so it can be reconstructed offline:
method—rvlfor RVL-compressed frames, orrawfor the fallback (see below).width,height— frame dimensions; the decoded frame haswidth*height16-bit pixels.encoding— the source ROS image encoding (e.g.16UC1).
To decode a frame: read byte_length bytes at byte_offset, then RVL-decode
into width*height little-endian uint16 pixels.
Fallback: if a payload can't be parsed as a 16-bit depth image, it is stored
verbatim with method=raw (the original serialized sensor_msgs/Image), so no
data is ever lost — those rows are decoded by parsing the ROS message directly.
Run the test suite:
make testRun tests for a specific package:
make test- Linting:
make lint(requires golangci-lint) - Tidy dependencies:
make tidy