tf_tree

A transform tree engine: store time-stamped rigid-body transforms between named coordinate frames and answer "where was frame A relative to frame B at time t?" — fast enough to sit inside a control loop, with diagnostics good enough to debug at 3 a.m.

Linux-first. The single-process engine is portable Rust and much of it compiles elsewhere; everything that maps memory — attaching to a live arena, the frozen .tft backend, tf_tree freeze — is Linux-only and behind a default-off shm feature. That sentence is here rather than in SUPPORT.md alone because nobody should meet it as a build error.

Start where you change nothing

Point it at a recording you already have. No node joins anyone's launch file, no robot is redeployed, and doctor --from-bag needs no features at all:

git clone https://github.com/NoeFontana/tf_tree && cd tf_tree
cargo install --path crates/tf_tree_cli --features shm   # shm: `freeze` maps memory

tf_tree doctor --from-bag drive.mcap                # what is wrong with this /tf traffic
tf_tree freeze --from-bag drive.mcap -o drive.tft   # keep the answer

drive.tft is a frozen transform index, and it is the arena itself written to disk. There are no pointers anywhere in the arena — every internal reference is an offset — so opening one is an mmap, with no parsing, no deserialization and no fixups (PHASE5 §2.1). Sixteen dataloader workers map the same file, the kernel charges the shared clean pages exactly once and the untouched ones not at all (measured basis: PHASE2 §3.8), and each worker queries in its own address space: no IPC, and no ROS node inside the training loop.

import numpy as np, tf_tree

# Open per worker, after the fork/spawn — docs/PHASE5.md §4.3 says why.
tree   = tf_tree.open_file("drive.tft")
plan   = tree.plan("base_link", "lidar_top")           # compile the route once
stamps = np.asarray(batch_stamps_ns, dtype=np.int64)   # integer nanoseconds
poses  = plan.at(stamps, layout="quat_twist")          # (N, 13) float64

The whole batch is one call into Rust. layout="quat_twist" appends the body twist to each pose, in the plan's source frame: it is the analytic derivative of the same interpolation the pose came from, not a finite difference between two lookups.

docs/PHASE5.md §2.2 is where that argument is made in the project's own words. A perception dataloader today does one of three bad things — re-parses the bag in every worker, precomputes poses into a pickle and loses the ability to query at arbitrary times, or runs a ROS node to serve transforms during training. This replaces all three and asks nobody to migrate anything, which is also why it is the part that shipped first.

Numbers belong where they can be reproduced, not in this section. just bench-report measures your host and writes report/{results.json,index.html}; the standing figures and their caveats are in docs/benchmarks/. Benchmarks, and what they are worth below explains why a row there may legitimately read UNAVAILABLE.

Status

On crates.io from 0.0.1; on PyPI from 0.0.2. The five engine crates are published — cargo add tf_tree. The Python wheel starts at 0.0.2, because the 0.0.1 commit did not compile off Linux and no wheel for it exists or can; see CHANGELOG.md. 0.0.x promises nothing between releases — cargo treats every one as incompatible with every other, so pin exactly.

Phase What it is Status
1 Single-process engine: arena, seqlock buffers, plans, SE(3) math Implemented
2 Shared memory: rendezvous, fd passing, claims as leases, reaping Implemented, except the daemon/recorder surface (§9–§10) and §11.3's fault injection. §3.5's ownership migration has the protocol and not the trigger: kill the arena's owner and lookups keep being served, but no new process can join
3 Python bindings (PyO3, zero intermediate allocation) Implemented
4 C ABI, C++ wrapper, ROS 2 ingest bridge, derivatives Done, except §5.9's affinity knobs and §6.3's replay rows. at_with_derivatives, both headers, the header-only C++ wrapper with its CMake package, and both halves of the ingest bridge — the rclcpp package in ros/tf_tree_ros included. §7's benchmark gate is partial
5 Frozen .tft arena, bag ingestion, diagnostics, tf_tree top Mostly done. FORMAT_VERSION = 3, the frozen arena (§2), the offline Python API (§4), the §5 counters and tf_tree top — terminal and --web (§7) — all landed. Ingestion is MCAP only (§3); the TFT001–TFT019 catalogue reports all nineteen ids, of which sixteen can detect (§6). §8 is deliberately not built. §9's benchmark artifact and §10's release readiness are partial
6–8 Multi-host, tf2 compatibility shim, replication Not started; Phase 7 is gated by D21 and none of its four gates is met

The per-phase §0.0 tables in docs/ are the source of truth, not this one — PHASE2.md, PHASE4.md, PHASE5.md. If this table and one of those disagree, the phase document is right and this is stale.

CI runs again as of 2026-08-16, after a gap since 2026-07-23 that ended when this repository was made public. A green check is evidence once more — of what the jobs cover. Gate locally with just first; CI is the second opinion.

First five minutes, with no data at all

The block above assumes you have a recording. This one assumes only the clone:

just quickstart        # uv-managed interpreter + venv, with the extension installed into it
.venv/bin/python

import tf_tree

tree = tf_tree.build([("map", "base"), ("base", "cam")])
# stamp in integer nanoseconds; pose is [qw, qx, qy, qz, x, y, z]
tf_tree.push(tree, "base", "map", 1_000, [1.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0])
tf_tree.push(tree, "base", "map", 2_000, [1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 5.0])

print(tree.plan("map", "base").at(1_500)[:3, 3])   # -> [2. 3. 4.]

That is a real result and not a toy: the query lands halfway between two samples, so the printed translation is their interpolated midpoint, and plan() is the object you keep — compiling the route once and evaluating it many times is the whole shape of the fast path.

Two things surprise people, both deliberate:

  • Stamps are integer nanoseconds. There is no float-seconds overload. At a 2026 epoch the ULP of float64 seconds is 238 ns, so every interval in a 1 kHz stream is wrong after a round trip.
  • Nothing returns a view into shared memory. An edge's samples are a ring another process is overwriting, and correct reads go through a seqlock. "Zero copy" here means no intermediate allocation — use Plan.at_into to supply the destination.

Rust is cargo add tf_tree; Python is pip install transform_tree — the distribution name differs from the import name, because PyPI refuses tf_tree as too close to the existing tftree (0008 records the measurement). import tf_tree either way. just alone lists everything the repository can do.

Is this a tf2 replacement?

It is not tf2, not a fork of it, and not affiliated with ROS. It is an independent engine that solves the same problem with a different data structure, and it is deliberately named so that people looking for a tf2 alternative can find it (0008 records that decision).

There is no drop-in tf2_ros::Buffer shim, and building one is not scheduled. That is Phase 7, gated by D21 on operating evidence this project has not yet produced; docs/PHASE7.md is what such a shim would have to be — including the places it would deliberately refuse to reproduce tf2's behaviour — and its §0.0 lists four gates, none of them met. What exists today instead is the ingest bridge (docs/PHASE4.md §5): a node that subscribes to /tf and fills an arena, which is a one-way seam and not a compatibility layer.

Two things worth stating precisely, because the loose versions of both are wrong:

  • The documented tf2 cost is listener and buffer CPU, per node — not /tf bandwidth. Autoware's ManagedTransformBuffer (in autoware_universe) reports taking a LiDAR sensing pipeline from 13 TF listener nodes to 0 — four per-sensor legs at 3 each plus the concatenation node (autowarefoundation/autoware#5385; the upstream discussion about moving it into tf2 is ros2/geometry2#758). That is a third-party report about their own stack, not a tf_tree measurement, and it is cited for the shape of the problem this engine's process model addresses. No claim is made about /tf bandwidth: no quantified public source for one exists.
  • Errors are Copy identifiers a program can branch on, with the prose in a separate layer (docs/API.md R5) — a lookup failure names the offending edge as data, not as a formatted string. That is a durable API-shape difference and deliberately not a claim about any particular tf2 defect; the misattributed-extrapolation one people cite (ros2/geometry2#832) was fixed by PR #896, merged 2026-03-18 and backported to Kilted, Jazzy and Humble (#897–#899). Marketing against a bug somebody already fixed is how a README goes stale in public.

Shared memory IPC is not a sandbox

Processes sharing a tf_tree arena are mutually trusting, same-user, cooperating processes. A read-write participant can corrupt any part of the arena, and no checksum would change that — it holds a writable mapping of the same pages (PHASE2.md §3.10). Do not attach a process you would not run as yourself. SECURITY.md draws the line between this and an actual vulnerability.

Three things the design does guarantee, and they are the ones that matter on a robot:

  • A read-only participant cannot corrupt anything, enforced by the MMU, not by convention. It is the default for consumers (D18), and it converts a class of whole-system failures into a single-process fault.
  • A participant that crashes, at any instruction, cannot corrupt the arena or wedge anyone else. A killed writer's edge is reclaimed; a killed interner's entry is recoverable; a killed mutator does not leave a permanently locked topology.
  • A participant that hangs cannot be mistaken for a crashed one. Liveness is the kernel's answer about a file lock, not a heartbeat timeout, so a SIGSTOPped publisher keeps its claims and a stalled one is never reaped out from under itself.

fork() is the sharp edge worth knowing about up front: the arena is mapped MADV_DONTFORK, so a child has no mapping and every inherited handle reports ChildDetached. Python's multiprocessing defaults to fork on Linux — open inside the worker, or use the spawn start method. A frozen .tft is the deliberate exception: it is a private read-only mapping, a child inherits it intact, and poisoning it would break multiprocessing for offline users to defend against a hazard they do not have (docs/PHASE5.md §4.3).

Workspace

crates/
├── tf_tree_math/    no_std SE(3)/SO(3) + dual quaternions; #![forbid(unsafe_code)]
├── tf_tree_arena/   no_std+alloc pointer-free arena + layout math
├── tf_tree_core/    no_std+alloc engine: interning, topology, seqlock buffers, plans
├── tf_tree/         std facade: builder, plan-cached lookup, Display errors
├── tf_tree_ipc/     zero-config rendezvous: runtime dir, OFD lock file, attach protocol
├── tf_tree_c/       C ABI + header-only C++ wrapper
├── tf_tree_bridge/  the ROS-independent half of the /tf ingest bridge
├── tf_tree_ingest/  MCAP -> arena: the two passes behind `ingest` and `freeze --from-bag`
├── tf_tree_py/      PyO3 bindings — binds the Rust core directly, not the C ABI
├── tf_tree_bench/   criterion benches, tf2 differential harness, the §9 report
├── tf_tree_tf2_sys/ the tf2 side of that differential harness — needs a ROS 2 install
└── tf_tree_cli/     binary `tf_tree` (alias `tft`): tree / echo / doctor / top /
                     ingest / freeze / topology / participants / bench
ros/                 ament_cmake packages: the §5 rclcpp bridge, and the DDS comparison
xtask/               loom, miri, and bench-gate runners
docs/                PROJECT.md, API.md, PHASE1–5.md, RUNBOOK.md, benchmarks/, decisions/

tf_tree_py and tf_tree_tf2_sys are outside the cargo --workspace build on purpose — they link libpython and a ROS 2 install respectively, neither of which a clean checkout can assume. ros/ is outside it for the same reason and is not cargo at all. Each has just recipes of its own (just py-*, just ros-build / just ros-test, just tf2-check), which is what "gate locally" means for them.

Five crates are intended for crates.io: tf_tree, tf_tree_core, tf_tree_math, tf_tree_arena, tf_tree_ipc. The rest carry publish = false with the reason in their manifest.

Commands

just quickstart     # clean clone -> a Python REPL with the extension installed
just build          # cargo build --workspace --all-targets
just test           # nextest + doctests
just lint           # fmt --check + clippy -D warnings
just loom           # concurrency model checking
just miri           # UB checking (arena + core + the facade's one unsafe)
just bench          # benchmark suite + go/no-go gate
just bench-report   # the PHASE5 §9 artifact -> report/{results.json,index.html}
just bench-check    # the same artifact against the committed baseline
just shm-torture    # PHASE2 §11.4's multi-process soak (30 min; nightly)

just --list for everything.

Benchmarks, and what they are worth

just bench-report emits report/results.json and report/index.html with a full provenance header. It is built so it cannot print a number it has no right to: it probes the host, and a row it cannot measure fairly comes out UNAVAILABLE with the reason and the command that produces it on a host that can. On a 4-core development machine that means most rows are gaps — which is the correct output, not a broken tool. docs/PHASE5.md §9.3 is the rule it enforces, and it includes a "where tf_tree is worse" section, in the same table.

just bench-check re-runs it and compares against the committed baseline in crates/tf_tree_bench/baseline/results.json, failing if a claim was withdrawn, a row was dropped, the arena layout changed, or a directional number moved past the slack the baseline itself records. It compares claims, not hosts: CPU model, core count, kernel, governor, load and every reason string are ignored, so the gate means the same thing on any machine. just bench-baseline-update regenerates the baseline; that diff belongs in the commit that causes it.

Standing numbers and their caveats live in docs/benchmarks/.

Reading order

  1. docs/PROJECT.md — overview, architecture, roadmap, and the decision log D1–D22 in §5.
  2. docs/API.md — the cross-cutting contract: six rules (§1) every binding obeys, and the §7 checklist a new surface passes. It is not a phase and authorizes nothing on its own.
  3. The phase spec you care about: PHASE1 … PHASE5. Each opens with its own status table.
  4. docs/decisions/ — the records for things the phase specs do not cover.

Contributing and support

CONTRIBUTING.md · SUPPORT.md (response expectations, platform support, MSRV policy) · SECURITY.md · CODE_OF_CONDUCT.md

MSRV is 1.87. just msrv reads the number out of [workspace.package] rust-version, builds --locked on exactly that toolchain, and checks that every hand-written rust-version — and this line — still agrees with it.

Licence

Dual MIT / Apache-2.0, at your option. See NOTICE.

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Author: Noé Fontana

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Publisher: wheels.yml on NoeFontana/tf_tree

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0.0.5

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0.0.4

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0.0.3

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This release

0.0.2 This release

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