3D Vision
Daniel Morris | dmorris@msu.edu | http://www.egr.msu.edu/3dvision/
The 3D Vision Lab at MSU explores the boundaries of Computer Vision in a dynamically changing three dimensional world. It asks: how can we estimate properties of moving and stationary objects in the world given inherent ambiguities, noise and resolution limits of our sensors? A variety of two and three dimensional sensors are employed, as well as fusion of the sensors. Below are some of the problems that have been or are being worked on.
Obstacles and Foliage Discrimination for Lidar
Off-road mobile robot navigation can require discrimination of foliage from non-traversable obstacles. We are developing object discrimination technologies that can find navigation hazards, such as tree trunks, rocks, cones etc, that may be partially occluded by foliage.
Autonomous Vehicles: CANVAS
MSU recently acquired a drive-by-wire vehicle outfitted with a large array of heterogeneous sensors as part of the CANVAS program. We are working with other faculty on novel perception systems to enable robust driving in all conditions.
Object detection and tracking with Lidar
Autonomous vehicles of the future will need precise sensing of the world around them. LIDAR is a promising sensor and provides 3D point clouds of the world. Within these points clouds we seek for objects such as people and vehicles, track them and provide trajectory predictions. Components of this problem include clustering 3D points to objects, rejecting clutter objects, developing appropriate shape and motion models, and accounting for self-occlusions and scene-occlusions.
Object classification using LIDAR
While range measurements from LIDAR are precise, they are sparse at long range. As a result determining object shape and category can be difficult. We develop 3D shape-based object categorization methods to classify object types
Person tracking and motion analysis for medical applications
New RGBD sensors enable precise tracking of human motions. This can be used for medical appications such as home care.
Rough Terrain and Ground Segmentation
An important initial step in local scene understanding is to estimate the ground surface. In flat open areas this is straight forward, but in cluttered environments and in rough terrain in can be challenging to separate ground surfaces from other objects. We have recently developed a new robust ground measurement cost function that accounts for occlusions and clutter. When modeled with a Markov Random Field and optimized with Loopy Belief Propagation, it produces high-quality ground segmentations of LIDAR data.
Plant photosynthesis distribution
Experiments on crop breeding, selection and modification require knowledge of photosynthesis rates. Innovative sensor development and processing is needed to assess photosynthesis and its distribution on plant bodies. Work is beginning with MSU’s Plant Biology Lab to create new photosynthesis sensing methods.
