Spatial intelligence platform for multi-robot terrain mapping. Real-time SLAM, traversability analysis, temporal normalization, fleet learning.
PyTerrainMap is a proprietary, production-grade spatial intelligence platform for autonomous systems. Build 3D maps, real-time SLAM, traversability prediction, and fleet-wide terrain understanding.
The Problem:
- SLAM systems are brittle and hard to integrate
- No unified way to handle out-of-order sensor data
- Traversability prediction requires manual annotation
- Fleet learning from multiple robots is complex
The Solution:
- Production SLAM implementation
- Temporal normalization (5D: x,y,z,time,quality)
- Traversability modeling
- Fleet consensus and learning
- Persistent world knowledge
Result: Robust multi-robot mapping, 10x faster exploration, fleet-wide learning.
pip install pyterrainmap
# or with uv
uv pip install pyterrainmap- Python 3.10+
- Precompiled wheels for macOS, Linux
Proprietary-first distribution:
- ✅ Wheels-only via PyPI (no source code)
- ✅ Production-optimized spatial intelligence
- ✅ 525 comprehensive tests
- ✅ Used in production robotics systems
from pyterrainmap import SpatialGraph
# Initialize spatial graph
graph = SpatialGraph()
# Add sensor observations
graph.add_lidar_scan(
robot_id='robot_1',
timestamp=time.time(),
frame=lidar_frame,
pose=current_pose,
)
# Real-time SLAM
graph.update_slam()
# Query traversability
zone = graph.get_zone(x=10.5, y=20.3)
traversability = graph.predict_traversability(zone)
print(f"Can traverse? {traversability.is_passable}")
print(f"Difficulty: {traversability.difficulty}")
# Fleet consensus
fleet_graph = SpatialGraph.aggregate([
robot1_graph,
robot2_graph,
robot3_graph,
])
# Predict zones other robots should avoid
risky_zones = fleet_graph.identify_hazardous_zones()
for zone in risky_zones:
print(f"Zone {zone.id}: {zone.hazard_type} (confidence: {zone.confidence:.1%})")- 3D Reconstruction: Point clouds + occupancy grids
- Real-Time SLAM: Visual odometry + IMU fusion
- Temporal Normalization: 5D coordinate system (x,y,z,time,quality)
- Traversability Modeling: Prediction for unknown terrain
- Fleet Learning: Multi-robot consensus and sharing
- Knowledge Graphs: Persistent entity tracking
- Production Ready: 525 tests, real-time performance
- SLAM: 30+ FPS on modern hardware
- Traversability prediction: <100ms per zone
- Fleet aggregation: Real-time for 10+ robots
- Map size: Handles unlimited-scale environments
- 525 tests passing
- Production-grade — used in robotics systems
- Real-time — guaranteed latency bounds
For production deployments: mullassery@gmail.com
Version: 1.3.0
License: Proprietary
Distribution: Wheels-only via PyPI
Python: 3.10+
Built for production multi-robot systems.