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Map sparsification

multiagent-mapping-sheep edited this page Nov 27, 2017 · 1 revision

Maplab offers several algorithms designed to reduce the computational/memory requirements of the map. That's particularly important when you would like to cover large areas or deal with a large number of mapping sessions.

Let's start with an initial map - one of the maps of a multi-floor dataset: initial

Keyframing

To reduce the number of keyframes in the system (and, as a result, also remove some landmarks), you can use the keyframing command:

kfh

Keyframing is based on multiple criteria:

  • maximum distance: if translation > threshold then adds a keyframe,
  • maximum rotation: if rotation > threshold then adds a keyframe,
  • number of keyframes: forces keyframes at every n-th keyframe,
  • number of coobserved landmarks: adds a keyframe if the landmarks co-observed by two frames is smaller than a threshold.

Those parameters can be adapted using flags:

  • kf_distance_threshold_m, default 0.75
  • kf_rotation_threshold_deg, default 20
  • kf_every_nth_vertex, default 10
  • kf_min_shared_landmarks_obs, default 40

The resulting map should contain significantly fewer keyframes: kfh

Map summarization

Map summarization removes the landmarks using an Integer Linear Programming optimization, using lpsolve solver. It tries to keep the landmarks that are most often observed while also maintaining a good coverage over the entire area.

To summarize to maps and only keep 5000 best landmarks, you can call:

lsparsify --num_landmarks_to_keep 5000

This reduces the number of landmarks and reduces the size of the map: lsparsify

Exporting a localization summary map

Maplab introduces a concept of a compact localization summary map - a minimal map representation that our loopclosure algorithm can operate on. Basically, it consists of landmark 3D positions, observation descriptors and a covisibility graph.

To export a localization summary map that can be used by ROVIOLI, just use the command:

generate_summary_map_and_save_to_disk --summary_map_save_path your_summary_map
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