What is the state-of-the-art approach to detect moving objects from mobile robot with 2D Lidar only?

Question

Having a setup of a mobile robot with a 360° 2D lidar (1 plane). What is the current easiest approach to detect moving object with a high reliability from a moving robot? To make it simple, we consider an indoor application.

My current considerations:

1. Creating a SLAM occupancy map (e.g. Hector SLAM)
2. conducting a projection of the measurement points to the created 2D occupancy map from t-2. Points which are projected to the free space can be clustered as moving objects.

The issues of the above approach are the chicken-egg-problem of the new measurement points and reliability of the detection.

I've read the DATMO approach (detection and tracking moving object). However, the papers about DATMO are too complicated and too abstract for me.

Is there any reliable approach without creating a global map through SLAM? Comparing points from t and t-1 directly without estimating the robot movement/odometry is not a possible task.

Answer 1

Obviously this problem can get very complicated when you just consider a 2D lidar. So I'm gonna try to keep my answer simple. You will want to estimate the position of the robot (odometry) as a minimum in order to project everything into a global space, rather than the local vehicle space. You can technically do it locally, but I personally don't think it is clean and you will run into more issues.

If you wanted to just track a moving object, you can use something simple like k-means clustering to estimate the center of the moving objects based on N scan point. Anything that doesn't move in the global space between t and t-1 (within a reasonable margin of error) could be filtered out. Anything that does move can be estimated, flagged, and tracked with something like a kalman filter to cover potential misdetections or if the obstacle stops moving. Obviously this isn't a flawless idea, I'm just trying to give something to go off of based on your sensing capabilities.