Abstract: Surgical systems and methods to account for an obstacle at a surgical site. A localizer tracks the surgical site using a first tracking modality and a vision device tracks the surgical site using a second tracking modality different from the first tracking modality. Controller(s) determine, based on tracking data from the localizer, a first positional relationship between the localizer and the surgical site. The controller(s) determine, based on tracking data from the vision device, a second positional relationship between the vision device and the surgical site. The controller(s) compare the first and second positional relationships to identify a discrepancy wherein the second positional relationship fails to match the first positional relationship. The controller(s) evaluate the discrepancy to identify an object presents an obstacle at the surgical site and generate a response to account for the obstacle.

Abstract: Systems and methods are described herein wherein a localizer is configured to detect a position of a first object and a vision device is configured to generate a depth map of surfaces near the first object. A virtual model corresponding to the first object is accessed, and a positional relationship between the localizer and the vision device in a common coordinate system is identified. An expected depth map of the vision device is then generated based on the detected position of the first object, the virtual model, and the positional relationship. A portion of the actual depth map that fails to match the expected depth map is identified, and a second object is recognize based on the identified portion.

Abstract: Systems and methods are described herein wherein a localizer is configured to detect a position of a first object and a vision device is configured to generate a depth map of surfaces near the first object. A virtual model corresponding to the first object is accessed, and a positional relationship between the localizer and the vision device in a common coordinate system is identified. An expected depth map of the vision device is then generated based on the detected position of the first object, the virtual model, and the positional relationship. A portion of the actual depth map that fails to match the expected depth map is identified, and a second object is recognize based on the identified portion.