Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Jacopo Serafin

Matthew Derry

Sachithra Hemachandra

Jonathan A Daudelin

Systems, methods, and other embodiments described herein relate to detecting reflection artifacts in a point cloud. In one embodiment, a method includes generating a first convex hull for defining an outline of a first cluster of points in a depth image and generating a second convex hull for defining an outline of a second cluster of points in the depth image. The first cluster of points originates from a point cloud and the second cluster of points originates from the point cloud. The method includes determining whether the second cluster of points is a reflection artifact based on identifying if a portion of the second convex hull is enclosed in the first convex hull and a range of the second convex hull is larger than a range of the first convex hull.

Systems and methods for detecting a reflection artifact in a point cloud

Systems and methods for adjusting the dimensions of point clouds are described herein. In one example, a system includes a processor and a memory having instructions that, when executed by the processor, cause the processor to generate a bounding box using a point cloud generated by a sensor and adjust a dimension of the bounding box based on a magnitude of a viewing angle of a side of the bounding box with respect to the sensor.

System and method for adjusting bounding box dimensions

Systems and methods are described for optimizing bounding boxes generated using point cloud information. In one example, a method for optimizing the bounding box includes the step of using an iterative closest point (ICP) algorithm to transform the bounding box using a transformation. The point cloud acts as the reference for the ICP algorithm, while the bounding box acts as the source for the ICP algorithm.

System and method for optimizing a bounding box using an iterative closest point algorithm

In a point cloud data set represented by an image, a first object can be caused to be distinguished from a second object. Current positions of points in the point cloud data set can be arguments for functions of a set of functions. The set of functions can include attraction functions for the points and repulsion functions for the points. Results of the set of functions can be calculated. Based on the results, at least some of the points can be caused to move from the current positions to new positions. In the point cloud data set represented by the image with the at least some of the points being at the new positions, the first object can be distinguished from the second object.

Distinguishing, in a point cloud data set represented by an image, a first object from a second object

Systems and methods of tracking objects around an ego vehicle, using state estimation of the objects, where state estimation may include an estimate of a position, velocity, acceleration and bounding box size of the tracked object are disclosed. An object may be detected at one or more poses over time. For each pose, a first bounding box may be generated. The largest bounding box may be determined. For each pose, the first bounding box may be expanded to an expanded bounding box. The expanded bounding box may match the largest bounding box. For each pose, the center of the expanded bounding box may be determined. The object may be tracked according to the center of the expanded bounding box.

System and method for tracking objects using using expanded bounding box factors

Fusing online and mapped terrain estimates by using weighted grid cells that scales the values returned from online terrain and mapped terrain is disclosed. Previously mapped terrain data and online terrain data are fused and a grid having cells of a predetermined size is overlaid on the terrain map. Each cell may include terrain data based on weighted mapped terrain data and weighted online terrain data, where the weighting values for the mapped terrain data and for the online terrain data may be different. A fused terrain estimate may be a result of a weighted mean for each cell smoothed to reduce noise.

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Ann Arbor, MI, United States of America

Jacopo Serafin