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yodayoda
yodayoda

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Published in

Map for Robots

·Jan 5, 2022

On security and safety of HD maps

More autonomous vehicles (AV) are driving on our roads every day. It’s time we discuss the security and in particular the cybersecurity and safety of high-definition (HD) maps. This topic is wildly ignored but could have terrible consequences and also slow down the adoption of the otherwise life-saving technology. …

Hd Maps

7 min read

On security and safety of HD maps
On security and safety of HD maps
Hd Maps

7 min read


Published in

Map for Robots

·Oct 20, 2021

All you need to know about the ICCV 2021 map workshop

Summary of ICCV 2021 Workshop on Map-Based Localization for Autonomous Driving In case you couldn’t attend ICCV, let us give you a quick update on what you missed from — of course our favorite — the workshop on AV mapping. The workshop was held on Oct 11th and chaired by…

Mapping

7 min read

All you need to know about the ICCV 2021 map workshop
All you need to know about the ICCV 2021 map workshop
Mapping

7 min read


Published in

Map for Robots

·Jul 20, 2021

Localization with Autoware

What you need to know about paths, transforms (TF) and other settings In this article, we will talk about how an autonomous vehicle can know its own location. But first, let’s start with a simple example. Introduction If you are driving a car in an unfamiliar place, you usually rely on…

Autoware

6 min read

Localization with Autoware
Localization with Autoware
Autoware

6 min read


Published in

Map for Robots

·Jun 24, 2021

Removing non-static objects from point clouds

Adapting LOAM to work with REMOVERT and running it on an example from nuScenes dataset Introduction Creating a map from from LiDAR scans is nowadays a standard process. It’s known as a process called point cloud registration. It’s done by stitching together several point clouds that are recorded in each instance…

Nuscenes

7 min read

Removing non-static objects from point clouds
Removing non-static objects from point clouds
Nuscenes

7 min read


Published in

Map for Robots

·Feb 3, 2021

Segmentation for Creating Maps

Image segmentation is one of the fundamental steps toward scene understanding of machines. By doing image segmentation, machines transit from the abstract image categorization toward more grounded pixel-level classification, in which each pixel is labeled by considering its local neighborhood, image context, scene composition as well as available low-level (pixel…

Segmentation

15 min read

Segmentation for Creating Maps
Segmentation for Creating Maps
Segmentation

15 min read


Published in

Map for Robots

·Dec 24, 2020

Why loop closure is so important for global mapping

Here we show visual slam for monocular videos and how a consistent map is obtained via loop closure mechanism. For self-driving cars to localize themselves in a global environment, accurate geometric maps are needed. Self-driving cars need to know in which lane they are driving but also how far away…

Slam

6 min read

Why loop closure is so important for global mapping
Why loop closure is so important for global mapping
Slam

6 min read


Published in

Map for Robots

·Sep 24, 2020

From depth map to point cloud

How to convert a RGBD image to points in 3D space This tutorial introduces the intrinsic matrix and walks you through how you can use it to convert an RGBD (red, blue, green, depth) image to 3D space. RGBD images can be obtained in many ways. E.g. from a system…

Depth

5 min read

From depth map to point cloud
From depth map to point cloud
Depth

5 min read


Published in

Map for Robots

·Sep 17, 2020

Geometric consistency as a learning signal

Self-supervised learning is all the rage these days for machine learning. Whether its models like GPT-3 for natural language processing or data augmentation for computer vision, everyone is trying to get something for free from their data without paying for expensive things like labels. In this article, we’ll go over…

3d

6 min read

Geometric consistency as a learning signal
Geometric consistency as a learning signal
3d

6 min read

yodayoda

yodayoda

147 Followers

A Map for Robots and a programmable world

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