## Method

Graph Based Road Estimation using Sparse 3D Points from Velodyne [la] [GRES3D+VELO]

Submitted on 2 Dec. 2014 17:34 by
Patrick Shinzato (Mobile Robotic Laboratory, ICMC - USP)

 Running time: 60 ms Environment: 4 cores @ 2.8 Ghz (C/C++)

 Method Description: Its a novel robust road estimation method that makes use of sparse 3D points projected in a screen plane. The main idea is to calculate a obstacle confidence degree for each point and then estimates the road area using a polar range histogram. Its free of several assumptions as flat surface and minimum height. Parameters: theta = 80 hist_precision = 5.0 Latex Bibtex: @phdthesis{Shinzato2015, author = {Patrick Yuri Shinzato}, title = {Estimation of obstacles and road area with sparse 3D points}, school = {Institute of Mathematics and Computer Science (ICMC) / University of Sao Paulo (USP)}, year = 2015, note = {http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07082015-100709/en.php} }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 85.43 % 83.04 % 82.69 % 88.37 % 8.43 % 11.63 % UMM_ROAD 88.19 % 88.65 % 83.98 % 92.85 % 19.48 % 7.15 % UU_ROAD 84.14 % 80.20 % 80.57 % 88.03 % 6.92 % 11.97 % URBAN_ROAD 86.07 % 84.34 % 82.16 % 90.38 % 10.81 % 9.62 %
This table as LaTeX

## Behavior Evaluation

 Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40
This table as LaTeX

The following plots show precision/recall curves for the bird's eye view evaluation.

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## Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.

## Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.

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