  Detecting moving vehicles and people is crucial for safe operation of UGVs
but is challenging in cluttered, real world environments. We propose a
registration technique that enables objects to be robustly matched and tracked,
and hence movers to be detected even in high clutter. Range data are acquired
using a 2D scanning Ladar from a moving platform. These are automatically
clustered into objects and modeled using a surface density function. A
Bhattacharya similarity is optimized to register subsequent views of each
object enabling good discrimination and tracking, and hence mover detection.
