Radar SLAM

Landmark management for radar SLAM

The aim of this project is to investigate the use of rotating synthetic aperture radar (RoSAR) in simultaneous localization and mapping (SLAM).

Typical SLAM Scene.

The first stage of this project has focused on developing a landmark management scheme [1] taking account of the following:

  • Landmarks contain multiple scattering/reflecting points
  • The angle between the landmark and the platform the sensor is mounted on, changes with the movement of the platform, resulting in varying numbers of radar detections over time
  • Clutter in the scene may result in false detections
At each time step the radar measurements are sifted, the selected measurements which are within a pre-defined distance of the existing landmarks are used for measurement association and to update the state.

Measurements which are not selected are used to determine if there are any new landmarks.

To determine whether an existing landmark should be deleted or an unconfirmed landmark should be confirmed we use M/N logic - if a landmark has been observed M times in the last N timesteps it is confirmed/deleted.
k = 1
EKF Prediction
Get radar measurements Zk
Calculate distances
landmarks to measurements
< threshold?
Associate measurements
Cluster measurements
Delete landmarks
Confirm landmarks
Merge landmarks
k = k + 1
Landmark management scheme, highlighted blocks are explained below.

Measurement Association

For each selected measurement
Calculate log distances
log distance
< threshold?
Update EKF state
Measurement association procedure.
The first step is to check whether the selected measurements can be associated with existing landmarks based on a log distance measure.

The associated measurements are used to update the EKF state.

Landmark Deletion

For each landmark in state
Landmark associated
with measurements?
Update landmark
Landmark within range?
Landmark meets M/N
logic criteria?
Delete landmark
Landmark deletion procedure.
In this step if a landmark is not associated with any measurements and is close enough to the sensor that it should be observed, we use M/N logic determine if the landmark should be deleted.

Deleting landmarks allows us to remove any which may have been incorrectly initialized or may have left the scene (i.e. a car has now left the car park).

Measurement Clustering

Cluster unselected measurements
For each cluster
Calculate distances landmarks to clusters
Distances < threshold?
Update landmark
Add cluster to unconfirmed landmarks
Measurements which were not initially selected as close to an existing landmark are clustered.

If a cluster is within a specified distance of an existing landmark it is used to update the landmark otherwise it is considered an unconfirmed landmark.
Measurements are sifted with each red diamond representing a landmark, remaining measurements are clustered, with measurements that do not belong to a cluster treated as false detections.

Landmark Confirmation

For each unconfirmed landmark
Number of measurements
> threshold?
Confirm landmark
Landmark meets M/N
logic criteria?
Confirm landmark
There are two ways of confirming a landmark:
  1. If the number of measurements associated with the unconfirmed landmark are above a defined threshold
  2. If the landmark has been observed often enough according to the M/N logic
Once a landmark is confirmed it is added to the system state.
Landmark confirmation rules.

Landmark Merging

Calculate distances between landmarks
Distances < threshold?
Merge landmarks
Finally, there is still a possibility that two landmarks in the system may in reality relate to the same landmark.

If the distance between two landmarks is less than a set threshold they are merged.

Example EKF-SLAM with Landmark Management

Example of landmark management using EKF SLAM


Orientation estimation code can be found here: SLAM Landmark Management


  1. Landmark Management in the Application of Radar SLAM
    Shuai Sun, Beth Jelfs, Kamran Ghorbani, Glenn Matthews, and Christopher Gilliam