Visualize SLAM Poses using SimpleViz - accumulates track, point clouds and map views

Overview

Beginning in ouster-sdk 0.13.0, it’s easier than ever to use “map” or “scan” accumulation to visualize lidar scans that contain pose information. The default visualizer, SimpleViz, present in both ouster-cli and Ouster SDK’s Python API support these accumulation modes out of the box.

Furthermore, when poses are not present in the LidarScan accumulation may still be useful to view the accumulated N scans from the live sensor/recording and reveal the accuracy/repeatability of the data.

Available view modes

There are three view modes of accumulation implemented in the default visualizer that may be enabled/disabled depending on its parameters and the data that is passed through it:

• poses mode, key 8 - all scan poses in a trajectory/path view (if poses data is present in scans.)

• map accumulation mode, key 7 - overall map view with select ratio of random points from every scan (available for scans with or without poses.)

• scan accumulation mode, key 6 - accumulated N scans (key frames) that is picked according to parameters (available for scans with or without poses.)

Key bindings

Keyboard controls available with SimpleViz:

Key	What it does
6	Toggle scans accumulation view mode (ACCUM)
7	Toggle overall map view mode (MAP)
8	Toggle poses/trajectory view mode (TRACK)
k / K	Cycle point cloud coloring mode of accumulated clouds or map
g / G	Cycle point cloud color palette of accumulated clouds or map
j / J	Increase/decrease point size of accumulated clouds or map

Use in Ouster SDK CLI

SimpleViz is accessible when visualizing data with ouster-cli using the viz command.

Here are the viz command options that affect it:

• --accum-num INTEGER - Accumulate up to this number of past scans for visualization. Use <= 0 for unlimited. Defaults to 100 if --accum-every or --accum-every-m is set.

• --accum-every INTEGER - Add a new scan to the accumulator every this number of scans.

• --accum-every-m FLOAT - Add a new scan to the accumulator after this many meters of travel.

• --map - If set, add random points from every scan into an overall map for visualization. Enabled if either --map-ratio or --map-size are set.

• --map-ratio R - Fraction of random points in every scan to add to overall map (0, 1]. [default: 0.01]

• --map-size N - Maximum number of points in overall map before discarding. [default: 1500000]

Examples of the CLI commands:

Dense accumulated clouds view (with every point of a scan)

To obtain the densest view use the --accum-num N --accum-every 1 parameters where N is the number of clouds to accumulate (N up to 100 is generally small enough to avoid slowing down the viz interface):

ouster-cli source OS-1-128_v3.0.1_1024x10_20230216_142857-000.pcap slam viz --accum-num 20

and the dense accumulated clouds result:

Dense view of 20 accumulated scans during the slam viz run

Overall map view (with poses)

One of the main task that we often needed was a preview of the overall map from the OSF with poses for example:

ouster-cli source OS-0-128_v3.0.1_1024x10_20230415_152307-000.osf viz --accum-num 20 \
--accum-every 0 --accum-every-m 10.5 --map -e stop

And here is the final result when viz is done and stopped (-e stop) after playing the whole file:

Data fully replayed with map and accum enabled (last current scan is displayed here in gray palette)

Data fully replayed with view only last 20 scans accumulated every 10.5 meters

Data fully replayed with view of only trajectory (yellow knobs is 20 accumulated key frames positions)

Programmatic use

To use any of these accumulation modes, provide their configuration directly to SimpleViz via keyword arguments. The following snippet will play back scans from the source scans_w_poses and the sensor configuration provided by meta:

import sys
from ouster.sdk import open_source
from ouster.sdk.viz import SimpleViz
from ouster.sdk.mapping import KissBackend

source_uri = sys.argv[1]
source = open_source(source_uri)

kiss_icp = KissBackend([source.metadata])


def scans_w_poses():
    for scan in source:
        yield kiss_icp.update([scan])


viz = SimpleViz(
    source.metadata,
    accum_max_num=100,
    accum_min_dist_num=0,
    accum_min_dist_meters=4,
    rate=1,
    on_eof='stop'
)

viz.run(scans_w_poses())

Alternatively, LidarScanViz (which is a lower-level visualizer that implements SimpleViz) can display a static map from scans that have poses computed in a preprocessing step:

import sys
from tqdm import tqdm  # for progress bar
from ouster.sdk import open_source
from ouster.sdk.viz import LidarScanViz
from ouster.sdk.viz.accumulators_config import LidarScanVizAccumulatorsConfig
from ouster.sdk.mapping import KissBackend

source_uri = sys.argv[1]
source = open_source(source_uri)

kiss_icp = KissBackend([source.metadata])

num_scans_to_map = 200
scans_w_poses = [
    kiss_icp.update([scan]) for _, scan in
    zip(tqdm(range(num_scans_to_map), desc="Computing map"), source)
]

viz = LidarScanViz(
    [source.metadata],
    accumulators_config = LidarScanVizAccumulatorsConfig(
        accum_max_num=100,
        accum_min_dist_num=0,
        accum_min_dist_meters=4
    )
)

for scan in scans_w_poses:
    viz.update(scan)

viz.draw(update=True)
viz.run()