enuxia-vacuum/dhruva-slam/README.md

DhruvaSLAM — Enuxia Vacuum mapping stack

DhruvaSLAM is the current SLAM, mapping and robot-state backend used by Enuxia Vacuum.

It originates from the upstream VacuumTiger project and is being adapted by Enuxia around real-world CRL-200S / Proscenic M6 Pro testing.

The current codebase is a validated development baseline, not the final navigation architecture.

---

Responsibilities

DhruvaSLAM currently handles:

• wheel odometry;
• LiDAR preprocessing;
• scan matching;
• occupancy-grid mapping;
• submaps;
• keyframes;
• loop closure;
• pose-graph optimization;
• map persistence;
• telemetry / map streaming;
• exploration and navigation baseline logic.

SangamIO
   │
   ├── wheel / sensors
   └── LiDAR
        │
        ▼
   DhruvaSLAM
        │
   ┌────┴─────┐
   ▼          ▼
 Pose        Map
   │          │
   └────┬─────┘
        ▼
 Navigation / diagnostics

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Enuxia baseline work

The Enuxia M6 Pro baseline contains a set of fixes developed from real robot runs, including:

• configurable minimum LiDAR scan size;
• full initial 360° scan before autonomous exploration;
• corrected treatment of unknown space during planning;
• navigation invalidation after SLAM graph corrections;
• submap-origin corrections after pose-graph optimization;
• persistent bumper obstacles;
• improved frontier failure tracking;
• protection against repeated failed frontier selections;
• safer stuck/failure termination.

These changes improved the baseline substantially, but they also exposed architectural limitations in the current navigator.

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Navigation status

The existing navigation code is intentionally considered legacy/baseline navigation.

The next Enuxia milestone is Navigation V2.

Navigation V2 target

Goal
 ↓
Global planner
Theta*
 ↓
Global path
 ↓
Layered local costmap
 ↓
DWA local planner
 ↓
cmd_vel
 ↓
SangamIO

Navigation V2 will also introduce:

• footprint-aware collision checking;
• measured progress detection;
• encoder-based stuck detection;
• contact-memory from bumpers;
• recovery based on actual displacement rather than timers;
• clean separation between navigation and exploration.

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Build

cargo test
cargo build --release

Run with an explicit config:

RUST_LOG=info \
./target/release/dhruva-slam \
  --config /path/to/dhruva-slam.toml

Do not commit deployment-specific network addresses or credentials into the public repository.

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Development philosophy

SLAM answers:

Where is the robot, and what does the world look like?

Navigation answers:

How should the robot move safely from here to there?

Exploration answers:

Where should the robot go next to learn more about the environment?

Cleaning answers:

How should the known floor be covered efficiently?

Those responsibilities are being separated progressively in the Enuxia fork.

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Origin

DhruvaSLAM originates from VacuumTiger.

Enuxia preserves upstream history and licensing while developing the M6 Pro integration and future navigation architecture independently.