SLAM Quickstart
This guide provides examples of using the SLAM API for development purposes. Users can run SLAM with an OS sensor’s hostname or IP for real-time processing, or with a recorded PCAP/OSF file for offline processing.
Obtain Lidar Pose and Calculate Pose Difference
The SLAM API outputs the sensor’s pose for each lidar scan, which you can use to determine the sensor’s orientation in your system. From the Lidar Poses, we can calculate the Pose difference between consecutive scans
from ouster.sdk import open_source
from ouster.sdk.mapping.slam import KissBackend
import numpy as np
import ouster.sdk.client as client
source_file_path = "/PATH_TO_THE_FILE"
data_source = open_source(source_file_path, sensor_idx=-1)
slam = KissBackend(data_source.metadata, max_range=75, min_range=1, voxel_size=1.0)
last_scan_pose = np.eye(4)
for idx, scans in enumerate(data_source):
# slam.update takes a list of scans as input and returns a list of scans,
# supporting potential multi-sensor configurations
scans_w_poses = slam.update(scans)
if not scans_w_poses:
continue
# use the first scan for calculation
col = client.first_valid_column(scans_w_poses[0])
# scans_w_poses[0].pose is a list of poses where each pose represents a column
# points' pose. Use the first valid scan's column pose as the scan pose
scan_pose = scans_w_poses[0].pose[col]
scan_ts = scans[0].timestamp[col]
print(f"idx = {idx} at timestamp {scan_ts} has the pose {scan_pose}")
# calculate the inverse transformation of the last scan pose
inverse_last = np.linalg.inv(last_scan_pose)
# calculate the pose difference by matrix multiplication
pose_diff = np.dot(inverse_last, scan_pose)
# extract rotation and translation
rotation_diff = pose_diff[:3, :3]
translation_diff = pose_diff[:3, 3]
print(f"idx = {idx} and Rotation Difference: {rotation_diff}, "
f"Translation Difference: {translation_diff}")
SLAM with Visualizer and Accumulated Scans
Visualizers and Accumulated Scans are also available for monitoring the performance of the algorithm, as well as for demonstration and feedback purposes.
from ouster.sdk import open_source
from ouster.sdk.viz import SimpleViz
from ouster.sdk.mapping.slam import KissBackend
source_file_path = "/PATH_TO_THE_FILE"
data_source = open_source(source_file_path, sensor_idx=-1)
slam = KissBackend(data_source.metadata, max_range=75, min_range=1, voxel_size=1.0)
scans_w_poses = map(lambda x: slam.update(x)[0], data_source)
SimpleViz(data_source.metadata, accum_max_num=10).run(scans_w_poses)
More details about the visualizer and accumulated scans can be found at the Ouster Visualizer and Scans Accumulator
Note
The performance of the SLAM algorithm depends on your CPU’s processing power and the ‘voxel_size’ parameter. Below is a suggestion for selecting an appropriate voxel size:
Intro SLAM in Ouster-CLI
We also offer a simpler method to run SLAM using the ouster-cli. For additional details, please refer to Ouster-CLI Mapping.