3D localization
of Objects
3D localization of objects using Pointclouds
(Automotive)
The Challenge
Vision is an essential component of any self-navigation system, such as an autonomous car. The more an autonomous system is able to understand its surroundings, the better it would be able to navigate its movements. 3D Object detection and localization play an important role in environmental understanding, as it will not only identify surrounding objects but also locate them with respect to them. A self-driving car continuously collects data from multiple sensors. In this case study, we used data collected from the Lidar sensor and 6 Camera Sensors to detect and localize surrounding cars using Lyft self-driving data published in 2019.
Approach
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Data Preparation: We Voxelized the Lidar Point Cloud and used the Bird’s eye view and cylindrically projected images for model input.

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Base Model: We used the Aggregate View Object Detector (AVOD), a two-staged detection network with slight modifications. The first stage of the model finds all the regions that are likely to have the object, and the second stage of the model refines those findings for more accurate predictions.

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