What Datasets Are Supported for Training and Evaluation in LingBot-Map?

LingBot-Map provides a unified dataset framework supporting ten benchmarks—including KITTI Odometry, TUM RGB-D, 7-Scenes, and ETH3D—through a pluggable BaseDataset architecture that enables seamless training and evaluation via YAML configuration.

LingBot-Map, from the Robbyant/lingbot-map repository, implements a modular dataset ecosystem designed for robotics and computer vision research. The framework standardizes how training and evaluation data is ingested, allowing researchers to swap between indoor RGB-D sequences, outdoor odometry benchmarks, and synthetic datasets without modifying core pipeline code.

The BaseDataset Abstraction

All supported datasets inherit from BaseDataset, defined in benchmark/dataset/base.py. This abstraction enforces a uniform API that decouples data loading from mapping algorithms.

The base class requires four core methods:

• get_scenes() – Returns a list of scene identifiers (e.g., sequence IDs or video names).
• get_frame_list(scene) – Returns integer-indexed frame IDs for a specific scene.
• load_frame_data(scene, frame_id) – Loads RGB images, poses, intrinsics, and depth for a single frame.
• load_global_data(scene) – Optional method for per-scene data such as global point clouds.

All loaders optionally support COLMAP-based pose and intrinsics extraction and on-the-fly video frame extraction, implemented primarily in the GeneralDataset class.

Supported Training and Evaluation Datasets

LingBot-Map ships with ten pre-built dataset loaders located in benchmark/datasets/. Each loader is automatically discoverable via the ConfigManager in benchmark/benchmark/core/config.py when a matching YAML file is placed in configs/datasets/.

The following datasets are supported for training and evaluation:

• GeneralDataset (benchmark/datasets/general.py) – Handles arbitrary image folders or video files for quick experiments.
• KittiDataset (benchmark/datasets/kitti.py) – Loads the KITTI Odometry benchmark for outdoor SLAM and visual odometry.
• VbrDataset (benchmark/datasets/vbr.py) – Supports the Vision Benchmark in Rome (VBR) indoor RGB sequences with TUM-style trajectories.
• TumDataset (benchmark/datasets/tum.py) – Implements the classic TUM RGB-D Freiburg indoor dataset.
• SevenScenesDataset (benchmark/datasets/seven_scenes.py) – Loads small-scale indoor RGB-D scenes used for relocalization tasks.
• TntDataset (benchmark/datasets/tnt.py) – Supports the Trajectory-Net-T large-scale outdoor RGB-D dataset with SfM-reconstructed poses.
• OxfordSpirtesDataset (benchmark/datasets/oxford_spires.py) – Handles aerial-style RGB-D sequences collected over Oxford.
• NeuralRgbdDataset (benchmark/datasets/neural_rgbd.py) – Loads synthetic RGB-D data generated by neural rendering pipelines.
• Eth3dDataset (benchmark/datasets/eth3d.py) – Imports high-precision indoor and outdoor multi-view data from ETH3D.
• DroidWDataset (benchmark/datasets/droid_w.py) – Processes visual odometry sequences recorded on a handheld rig.

How to Load Datasets in LingBot-Map

Direct Python Instantiation

You can instantiate any dataset directly by importing its class and providing the root data path. The following example demonstrates loading KITTI sequences:

from benchmark.datasets.kitti import KittiDataset

# Initialize the KITTI dataset

kitti = KittiDataset(
    raw_data_root="/data/kitti_odometry",
    sequences=["00", "01"],      # Optional whitelist of sequences

    target_size=[640, 480],       # Optional resizing (must be multiples of 14)

)

# Iterate through frames

scene = kitti.get_scenes()[0]     # e.g., "00"

for frame_id in kitti.get_frame_list(scene):
    data = kitti.load_frame_data(scene, frame_id)
    rgb = data["rgb"]            # H×W×3 uint8 array

    pose = data.get("pose")      # 4×4 float32 transformation matrix

    intrinsics = data["intrinsics"]  # [fx, fy, cx, cy]

    # Feed (rgb, pose, intrinsics) into your mapping pipeline

Configuration-Driven Loading

The typical workflow uses ConfigManager to resolve dataset configurations from YAML files. This approach is used by the benchmark runner in benchmark/run.py:

from benchmark.benchmark.core.config import ConfigManager
from pathlib import Path

# Load base configuration

cfg = ConfigManager(Path("configs/base.yaml"))

# Resolve specific dataset configuration

ds_cfg = cfg.get_dataset_config("kitti")

# Returns: {"dataset_class": "kitti", "params": {...}, ...}

# Dynamically import and instantiate

module = __import__(f"benchmark.datasets.{ds_cfg['dataset_class']}", fromlist=["*"])
DatasetCls = getattr(module, f"{ds_cfg['dataset_class'].title()}Dataset")
dataset = DatasetCls(**ds_cfg["params"])

The configuration file configs/base.yaml lists active datasets under the datasets key, where each entry corresponds to a YAML file in configs/datasets/:

# configs/base.yaml

workspace: /tmp/lingbot-workspace
datasets:
  - kitti      # Refers to configs/datasets/kitti.yaml

  - tum_rgbd   # Refers to configs/datasets/tum_rgbd.yaml

methods:
  - lingbot_map

Extending the Framework with Custom Datasets

Adding a new dataset requires two files: a YAML configuration and a Python loader that inherits from BaseDataset. The framework automatically discovers the new dataset when placed in the correct directories.

Create a configuration file:

# configs/datasets/my_custom.yaml

dataset: my_custom          # Must match the Python module name

_use_colmap: true
load_img_size: 640

Implement the loader:

# benchmark/datasets/my_custom.py

from benchmark.dataset.base import BaseDataset

class MyCustomDataset(BaseDataset):
    def get_scenes(self):
        return ["scene_1", "scene_2"]
    
    def get_frame_list(self, scene):
        # Return list of frame indices

        return list(range(100))
    
    def load_frame_data(self, scene, frame_id):
        # Load and return RGB, pose, intrinsics

        return {"rgb": ..., "pose": ..., "intrinsics": ...}

After placing these files in the repository, the ConfigManager automatically registers the dataset for training and evaluation.

Summary

• LingBot-Map supports ten built-in datasets ranging from outdoor odometry (KITTI) to indoor RGB-D (TUM, 7-Scenes, ETH3D) and synthetic data (Neural-RGB-D).
• All datasets implement the BaseDataset interface in benchmark/dataset/base.py, providing standardized methods like get_scenes() and load_frame_data().
• The ConfigManager in benchmark/benchmark/core/config.py handles automatic discovery and instantiation via YAML configurations in configs/datasets/.
• Researchers can add custom datasets by creating a Python loader inheriting from BaseDataset and a corresponding YAML config file.

Frequently Asked Questions

How do I configure a dataset for evaluation in LingBot-Map?

Add the dataset name to the datasets list in configs/base.yaml, ensuring a matching YAML file exists in configs/datasets/. The ConfigManager validates the configuration and instantiates the corresponding loader class when you run benchmark/run.py.

What is the difference between GeneralDataset and specialized loaders?

GeneralDataset in benchmark/datasets/general.py provides a flexible loader for arbitrary image folders or video files with optional COLMAP pose extraction. Specialized loaders like KittiDataset or TumDataset implement dataset-specific parsing logic for ground-truth trajectories and calibration files, offering higher accuracy for benchmark evaluation.

Does LingBot-Map support custom video files for training?

Yes. The GeneralDataset class supports on-the-fly video frame extraction, allowing you to point the configuration to a video file or folder of images. It optionally extracts poses using COLMAP if ground-truth data is unavailable.

Where are dataset configurations stored in the repository?

Dataset YAML configurations reside in configs/datasets/, while the Python loader implementations are located in benchmark/datasets/. The base configuration in configs/base.yaml defines which datasets are active for a specific training or evaluation run.

{
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    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
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}