LaMAR
Benchmarking Localization and Mapping
for Augmented Reality

Paul-Edouard Sarlin* · Mihai Dusmanu*
Johannes L. Schönberger · Pablo Speciale · Lukas Gruber · Viktor Larsson · Ondrej Miksik · Marc Pollefeys

ECCV 2022

Project Page | Video

LaMAR includes multi-sensor streams recorded by AR devices along hundreds of unconstrained trajectories captured over 2 years in 3 large indoor+outdoor locations.

This repository hosts the source code for LaMAR, a new benchmark for localization and mapping with AR devices in realistic conditions. The contributions of this work are:

1. A dataset: multi-sensor data streams captured by AR devices and laser scanners
2. scantools: a processing pipeline to register different user sessions together
3. A benchmark: a framework to evaluate algorithms for localization and mapping

See our ECCV 2022 tutorial for an overview of LaMAR and of the state of the art of localization and mapping for AR.

Overview

This codebase is composed of the following modules:

• lamar: evaluation pipeline and baselines for localization and mapping
• scantools: data API, processing tools and pipeline
• ScanCapture: a data recording app for Apple devices

Data format

We introduce a new data format, called Capture, to handle multi-session and multi-sensor data recorded by different devices. A Capture object corresponds to a capture location. It is composed of multiple sessions and each of them corresponds to a data recording by a given device. Each sessions stores the raw sensor data, calibration, poses, and all assets generated during the processing.

from scantools.capture import Capture
capture = Capture.load('data/CAB/')
print(capture.sessions.keys())
session = capture.sessions[session_id]  # each session has a unique id
print(session.sensors.keys())  # each sensor has a unique id
print(session.rigs)  # extrinsic calibration between sensors
keys = session.trajectories.key_pairs()  # all (timestamp, sensor_or_rig_id)
T_w_i = sessions.trajectories[keys[0]]  # first pose, from sensor/rig to world

More details are provided in the specification document CAPTURE.md.

Installation

1️⃣ Install the core dependencies using the provided script, tested on Ubuntu 22.04:

scripts/install_core_dependencies.sh

Alternatively, you can install them manually in the following order:

• Python 3.9 / 3.10 (we recommend using a venv virtual environment).
• Ceres Solver 2.1
• Colmap 3.8 built from source. Note: Do not install libceres-dev as it was installed in the previous step.
• hloc 1.4 and its dependencies

2️⃣ Install LaMAR libraries as editable packages:

python -m pip install -e .

3️⃣ Optional: the processing pipeline additionally relies on heavier dependencies not required for benchmarking:

• Pip dependencies: python -m pip install -e .[scantools]
• raybender for raytracing
• pcdmeshing for pointcloud meshing

4️⃣ Optional: if you wish to contribute, install the development tools as well:

python -m pip install -e .[dev]

Docker images

The Dockerfile provided in this project has multiple stages, two of which are: scantools and lamar.

Building the Docker Images

You can build the Docker images for these stages using the following commands:

# Build the 'scantools' stage
docker build --target scantools -t lamar:scantools -f Dockerfile ./

# Build the 'lamar' stage
docker build --target lamar -t lamar:lamar -f Dockerfile ./

Pulling the Docker Images from GitHub Docker Registry

Alternatively, if you don't want to build the images yourself, you can pull them from the GitHub Docker Registry using the following commands:

# Pull the 'scantools' image
docker pull ghcr.io/microsoft/lamar-benchmark/scantools:latest

# Pull the 'lamar' image
docker pull ghcr.io/microsoft/lamar-benchmark/lamar:latest

Usage of docker images

To use the lamar Docker image, you can follow these steps:

1. Set the DATA_DIR and DOCKER_RUN environment variables:

export DATA_DIR=/path/to/data
export DOCKER_RUN="docker run -it --rm --init -u $(id -u):$(id -g) -v ${DATA_DIR}:${DATA_DIR} ghcr.io/microsoft/lamar-benchmark/lamar:latest "

Note: replace ghcr.io/microsoft/lamar-benchmark/lamar:latest with lamar:lamar if you want to use the image you built locally.

2. Run the desired command inside the Docker container, for example:

$DOCKER_RUN ls $DATA_DIR
$DOCKER_RUN python pipelines/pipeline_navvis_rig.py --help

The DOCKER_RUN variable is a prefix to the command you would run if the code were installed locally. This ensures the command runs inside the Docker container. The DATA_DIR will be mounted as a volume representing the same folder on the local machine, and the user/group will match the local environment to avoid having the output as root.

Benchmark

1️⃣ Obtain the evaluation data: visit the dataset page and place the 3 scenes in ./data :

data/
├── CAB/
│   └── sessions/
│       ├── map/                # mapping session
│       ├── query_hololens/     # HoloLens test queries
│       ├── query_phone/        # Phone test queries
│       ├── query_val_hololens/ # HoloLens validation queries
│       └── query_val_phone/    # Phone validation queries
├── HGE
│   └── ...
└── LIN
    └── ...

Each scene contains a mapping session and queries for each device type. We provide a small set of validation queries with known ground-truth poses such that they can be used for developing algorithms and tuning parameters. We keep private the ground-truth poses of the test queries.

2️⃣ Run the single-frame evaluation with the strongest baseline:

python -m lamar.run \
	--scene $SCENE --ref_id map --query_id $QUERY_ID \
	--retrieval fusion --feature superpoint --matcher superglue

where $SCENE is in {CAB,HGE,LIN} and $QUERY_ID is in {query_phone,query_hololens} for testing and in {query_val_phone,query_val_hololens} for validation. All outputs are written to ./outputs/ by default. For example, to localize validation Phone queries in the CAB scene:

python -m lamar.run \
	--scene CAB --ref_id map --query_id query_val_phone \
	--retrieval fusion --feature superpoint --matcher superglue

This executes two steps:

1. Create a sparse 3D map using the mapping session via feature extraction, pair selection, feature matching, triangulation
2. Localize each image of the sequence via feature extraction, pair selection, feature matching, absolute pose estimation

3️⃣ Obtain the evaluation results:

• validation queries: the script print the localization recall.
• test queries: until the benchmark leaderboard is up and running, please send the predicted pose files to [email protected] ⚠️ we will only accept at most 2 submissions per user per week.

4️⃣ Workflow: the benchmarking pipeline is designed such that

• the mapping and localization process is split into modular steps listed in lamar/tasks/
• outputs like features and matches are cached and re-used over multiple similar runs
• changing a configuration entry automatically triggers the recomputation of all downstream steps that depend on it

Other evaluation options

[Click to expand]

Using radio signals for place recognition:

python -m lamar.run [...] --use_radios

Localization with sequences of 10 seconds instead of single images:

python -m lamar.run [...] --sequence_length_seconds 10

Adding your own algorithms

[Click to expand]

To add a new local feature:

• add your feature extractor to hloc in hloc/extractors/myfeature.py
• create a configuration entry in lamar.tasks.feature_extraction.FeatureExtraction.methods

To add a new global feature for image retrieval:

• add your feature extractor to hloc in hloc/extractors/myfeature.py
• create a configuration entry in lamar.tasks.feature_extraction.RetrievalFeatureExtraction.methods

To add a new local feature matcher:

• add your feature matcher to hloc in hloc/matchers/mymatcher.py
• create a configuration entry in lamar.tasks.feature_matching.RetrievalFeatureMatching.methods

To add a new pose solver: create a new class that inherits from lamar.tasks.pose_estimation.SingleImagePoseEstimation:

class MyPoseEstimation(SingleImagePoseEstimation):
    method = {'name': 'my_estimator'}
    def run(self, capture):
        ...

Processing pipeline

Each step of the pipeline corresponds to a runfile in scantools/run_*.py that can be used as follow:

• executed from the command line: python -m scantools.run_phone_to_capture [--args]
• imported as a library:

from scantools import run_phone_to_capture
run_phone_to_capture.run(...)

We provide pipeline scripts that execute all necessary steps:

• pipelines/pipeline_scans.py aligns multiple NavVis sessions and merge them into a unique reference session
• pipelines/pipeline_sequence.py aligns all AR sequences to the reference session

The raw data will be released soon such that anyone is able to run the processing pipeline without access to capture devices.

Here are runfiles that could be handy for importing and exporting data:

• run_phone_to_capture: convert a ScanCapture recording into a Capture session
• run_navvis_to_capture: convert a NavVis recording into a Capture Session
• run_session_to_kapture: convert a Capture session into a Kapture instance
• run_capture_to_empty_colmap: convert a Capture session into an empty COLMAP model
• run_image_anonymization: anonymize faces and license plates using the Brighter.AI API
• run_radio_anonymization: anonymize radio signal IDs
• run_combine_sequences: combine multiple sequence sessions into a single session
• run_qrcode_detection: detect QR codes in images and store their poses

Raw data

We also release the raw original data, as recorded by the devices (HoloLens, phones, NavVis scanner), with minimal post-processing. Like the evaluation data, the raw data is accessed through the dataset page. More details are provided in the specification document RAW-DATA.md.

Release plan

We are still in the process of fully releasing LaMAR. Here is the release plan:

• LaMAR evaluation data and benchmark
• Ground truthing pipeline
• iOS capture app
• Full raw data
• Leaderboard and evaluation server
• 3D dataset viewer

BibTex citation

Please consider citing our work if you use any code from this repo or ideas presented in the paper:

@inproceedings{sarlin2022lamar,
  author    = {Paul-Edouard Sarlin and
               Mihai Dusmanu and
               Johannes L. Schönberger and
               Pablo Speciale and
               Lukas Gruber and
               Viktor Larsson and
               Ondrej Miksik and
               Marc Pollefeys},
  title     = {{LaMAR: Benchmarking Localization and Mapping for Augmented Reality}},
  booktitle = {ECCV},
  year      = {2022},
}

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