libgnss++

Modern C++17 GNSS toolkit for non-GUI positioning.

Native SPP, RTK, PPP, CLAS/MADOCA, RTCM, UBX, and direct QZSS L6 handling without an external RTKLIB runtime.

What You Get

• Solvers: gnss spp, gnss solve, gnss ppp
• Inputs/tools: RINEX, RTCM, UBX, SBF, NMEA, BINEX, QZSS L6
• Products: SP3, CLK, IONEX, DCB
• Extras: benchmarks, web dashboard, Python bindings, Docker, ROS 2 playback

Results

Area	Public comparison	Current result
RTK	PPC Tokyo/Nagoya vs RTKLIB demo5	+17.0 pp positioning, +28.1 pp official score, -11.96 m P95 H delta
CLAS PPP	QZSS CLAS vs CLASLIB	3.57 mm vs 7.29 mm RMS 3D
Urban RTK	UrbanNav Tokyo Odaiba vs RTKLIB demo5	More fixes, lower Hp95/Vp95; --preset odaiba closes Hmed
SPP	PPC SPP adaptive robust + policy gate	No P95 regression with <=1 pp positioning drop

PPC RTK vs RTKLIB demo5

These are public PPC Tokyo/Nagoya moving-RTK replays using the same rover/base/nav observations for libgnss++ and RTKLIB demo5.

Run	gnssplusplus Positioning	RTKLIB Positioning	Delta	gnssplusplus Fix	RTKLIB Fix	PPC official score	RTKLIB official score	Official delta	P95 H delta
Tokyo run1	90.0%	66.3%	+23.7 pp	54.4%	30.5%	34.9%	0.0%	+34.9 pp	+3.39 m
Tokyo run2	95.3%	84.3%	+11.0 pp	64.1%	27.6%	69.0%	16.9%	+52.1 pp	-18.51 m
Tokyo run3	95.7%	93.1%	+2.5 pp	63.0%	40.5%	60.6%	35.6%	+25.0 pp	-0.24 m
Nagoya run1	88.8%	65.8%	+23.0 pp	64.5%	33.8%	49.5%	22.4%	+27.1 pp	-23.78 m
Nagoya run2	85.6%	69.8%	+15.8 pp	51.4%	18.8%	20.9%	11.0%	+9.9 pp	-27.24 m
Nagoya run3	93.8%	67.7%	+26.1 pp	27.1%	13.9%	27.4%	7.6%	+19.7 pp	-5.37 m

Across these six public runs, the coverage profile averages +17.0 pp Positioning-rate lead, +28.1 pp PPC official-score lead, and -11.96 m P95 horizontal-error delta versus RTKLIB demo5.

CLAS PPP vs CLASLIB

QZSS CLAS PPP from raw L6 binary, 2019-08-27 static dataset, 1 hour (3599 epochs):

Metric	libgnss++ --claslib-parity	CLASLIB
Matched fixed epochs	3594 / 3599 (99.86%)	3594 / 3599 (99.86%)
RMS 3D fixed-only	3.57 mm	7.29 mm
3D bias mean offset	1.66 mm	4.84 mm
RMS East	1.15 mm	1.52 mm
RMS North	1.21 mm	0.92 mm
RMS Up	3.15 mm	7.07 mm
First fix epoch	epoch 6	epoch 6
Runtime dependency	no CLASLIB runtime	CLASLIB runtime

CLASLIB 2D	libgnss++ 2D

RTKLIB Odaiba 2D	libgnss++ Odaiba 2D

Quick Start

Choose the entrypoint that matches your job:

• Robotics quick start: RTK replay, local web inspection, ROS2 receiver launch, and rosbag capture.
• Research quick start: repeatable sign-off runs, profile comparisons, Python inspection, and artifact layout.
• Dataset gallery: current public dataset lanes and the adapter contract for adding more.

Build:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
python3 apps/gnss.py doctor
python3 apps/gnss.py ros2-doctor --device /dev/ttyUSB0
python3 apps/gnss.py ros2-bag-doctor --bag /path/to/rosbag --summary-json output/ros2_bag_doctor_summary.json
python3 apps/gnss.py field-report --out output/field_report.md
python3 apps/gnss.py robotics-smoke --profile realtime

ros2-bag-doctor reads sqlite ROS2 bags for message-level rates/gaps. For MCAP bags it uses the optional Python mcap package when available, and otherwise falls back to MCAP metadata.yaml for topic presence, counts, and duration. gnss web auto-discovers output/field_report*.json and shows the report links, Markdown preview, setup/ROS2/bag/smoke status, and next debug commands.

Run a solution:

python3 apps/gnss.py spp \
  --obs data/rover_static.obs \
  --nav data/navigation_static.nav \
  --out output/spp_solution.pos

RTK example:

python3 apps/gnss.py solve \
  --rover data/rover_kinematic.obs \
  --base data/base_kinematic.obs \
  --nav data/navigation_kinematic.nav \
  --mode kinematic \
  --out output/rtk_solution.pos

Run the web UI:

python3 apps/gnss.py web --port 8085

Then open http://127.0.0.1:8085.

List commands:

python3 apps/gnss.py --help

Docker

docker build -t libgnsspp:latest .
docker run --rm -it -p 8085:8085 -v "$PWD:/workspace" \
  libgnsspp:latest web --host 0.0.0.0 --port 8085 --root /workspace

Benchmarks

• Benchmarks
• Validation
• PPC reproduction commands
• SPP accuracy notes

Docs

• https://rsasaki0109.github.io/gnssplusplus-library/
• Documentation index
• Quick start
• Robotics quick start
• Research quick start
• Dataset gallery
• Interfaces
• Architecture
• Reference analyses
• Contributing

Install

cmake --install build --prefix /opt/libgnsspp
/opt/libgnsspp/bin/gnss --help

Tests

ctest --test-dir build --output-on-failure

License

MIT License. See LICENSE.