Generating object references from perception data in unstructured environments and related technology

A method comprising:

• receiving, at data-processing hardware operably associated with a mobile robot, perception data corresponding to an object in an environment of the mobile robot;
• generating, by the data-processing hardware and based at least partially on the perception data, a feature reference corresponding to a feature of the object;
• generating, by the data-processing hardware, candidate object references for the object based at least partially on different respective alignments between the feature reference and a model of the object, wherein generating the candidate object references includes aligning the model of the object with the feature reference at a positional offset;
• evaluating, by the data-processing hardware, the candidate object references for accuracy;
• selecting, by the data-processing hardware, one of the candidate object references based at least partially on a result of evaluating the candidate object references;
• and
• controlling, by the data-processing hardware, the mobile robot based at least partially on the selected one of the candidate object references.

A method comprising:

• receiving, at data-processing hardware operably associated with a mobile robot, perception data corresponding to an object in an environment of the mobile robot;
• generating, by the data-processing hardware, surface normals corresponding to the perception data;
• clustering, by the data-processing hardware, the perception data based at least partially on the surface normals;
• generating, by the data-processing hardware and based at least partially on the perception data, a feature reference corresponding to a feature of the object, wherein generating the feature reference includes generating the feature reference based at least partially on a result of clustering the perception data;
• generating, by the data-processing hardware, candidate object references for the object based at least partially on different respective alignments between the feature reference and a model of the object;
• evaluating, by the data-processing hardware, the candidate object references for accuracy;
• selecting, by the data-processing hardware, one of the candidate object references based at least partially on a result of evaluating the candidate object references;
• and
• controlling, by the data-processing hardware, the mobile robot based at least partially on the selected one of the candidate object references.