PyTerrainMap

Spatial intelligence platform for multi-robot terrain mapping. Real-time SLAM, traversability analysis, temporal normalization, fleet learning.

Product Overview

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.

Why Robotics Teams Choose This

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.

Installation

pip install pyterrainmap
# or with uv
uv pip install pyterrainmap

Requirements

• Python 3.10+
• Precompiled wheels for macOS, Linux

Distribution Model

Proprietary-first distribution:

• ✅ Wheels-only via PyPI (no source code)
• ✅ Production-optimized spatial intelligence
• ✅ 525 comprehensive tests
• ✅ Used in production robotics systems

Quick Start

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%})")

Features

• 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

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

Quality & Testing

• 525 tests passing
• Production-grade — used in robotics systems
• Real-time — guaranteed latency bounds

Support

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.