Abstract: The method can include: sampling images of a conveyor region S110; determining a set of patches S120; estimating a displacement for each patch S130; estimating a conveyor motion parameter(s) based on the patch displacement S140; and optionally performing an action based on the conveyor motion parameter S150. However, the method S100 can additionally or alternatively include any other suitable elements. The method S100 functions to estimate a set of conveyor motion parameters (e.g., conveyor speed). Additionally or alternatively, the method can function to enable dynamic control of an independent robotic assembly system(s) to accommodate conveyor motion changes.

Abstract: The method S100 can include: providing bowls within the workspace based on the assembly context S105; sampling sensor data for the workspace S110; detecting bowls based on the sensor data S120; determining a labeled training dataset S130; and training a classifier for the assembly context S140. However, the method S100 can additionally or alternatively include any other suitable elements.

Abstract: A method can include: receiving imaging data; identifying containers using an object detector; scheduling insertion based on the identified containers; and optionally performing an action based on a scheduled insertion. However, the method can additionally or alternatively include any other suitable elements. The method functions to schedule insertion for a robotic system (e.g., ingredient insertion of a robotic foodstuff assembly module). Additionally or alternatively, the method can function to facilitate execution of a dynamic insertion strategy; and/or facilitate independent operation of a plurality of robotic assembly modules along a conveyor line.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.