From 638c9b1cdef212ffcfd58bb23d4fc18c846de7ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Guido=20K=C3=BCppers?= Date: Fri, 13 Oct 2023 18:28:27 +0200 Subject: [PATCH] WIP on localization notebook --- .../8_localization.ipynb | 136 ++++++++++++------ 1 file changed, 92 insertions(+), 44 deletions(-) diff --git a/section_2_sensor_data_processing/8_localization.ipynb b/section_2_sensor_data_processing/8_localization.ipynb index 3902c69..fbc445f 100644 --- a/section_2_sensor_data_processing/8_localization.ipynb +++ b/section_2_sensor_data_processing/8_localization.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -318,47 +318,108 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "At first glance, the comparison of the trajectories in the overview view looks good. Only in the curves larger deviations are observable. These deviations are mainly due to the estimation of the vehicle yaw angle from two subsequent GNSS positions.\n", + "However, a look at the detailed views also shows that there are poor results in the pose estimates when driving straight ahead (in the area of $y \\approx 650 m$). In this area, LiDAR odometry could not provide sufficient motion estimate due to insufficient features. You should take into account that this is only noticeable because of the visualization of the actual measuring points in the plot. It therefore makes sense to visualize the actual measuring points at least in addition to the connecting lines.\n", + "\n", + "### Task: Visualizing the yaw-angle\n", + "\n", + "In the following, we will also take a brief look at the yaw angle, since this is not explicitly shown in the 2D plot.\n", + "There to fill in the gaps in the code-cell below.\n", + "\n", + "In this plot we want to plot the yaw angle over time. Two plots are to be displayed. On the one hand the ground-truth yaw angle and on the other hand the estimated yaw angle.\n", + "\n", + "##### __Hints__:\n", + "- The best way to get inspired is to look at the code example where we have plotted the trajectories x over y: `df.plot(x='x_ground_truth', y='y_ground_truth', ax=ax)`\n", + "- Time is denoted as `'t'` in the data frame\n", + "- The yaw angle is denoted as `'psi_'` in the data frame" + ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(1,1,figsize=(20, 4))\n", - "ax.set_xlim(310, 360)\n", - "ax.set_ylim(-200, 200)\n", + "### START CODE HERE ###\n", + "\n", + "\n", + "### Solution\n", "df.plot(x='t', y='psi_ground_truth', ax=ax)\n", "df.plot(x='t', y='psi_estimate', ax=ax)\n", + "### END CODE HERE ###\n", + "# Set legend labels\n", "ax.legend(['Ground Truth Trajectory', 'Estimated Trajectory'])\n", + "# Set axes limits\n", + "ax.set_xlim(310, 360)\n", + "ax.set_ylim(-200, 200)\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The plot shows that the vehicle is initially aligned with a yaw angle of approx. 180°. Since the yaw angle is defined here between -180° and 180°, there are discontinuities that can be seen in the plot. However, these are not really dramatic in this case. It gets interesting at the point where the vehicle stops at a stop sign. Here, the estimated yaw angle jumps even though the vehicle is stationary. This behavior is also due to the estimation based on two sequential GNSS positions, since these are very close to each other, but can slightly deviate from each other due to measurement errors, resulting in arbitrary yaw angle estimates.\n", + "\n", + "With these simple analysis, we have already identified a major weakness of the implementation. This could be counteracted, for example, by using an additional sensor for direct measurement of the vehicle orientation. An example of such a sensor is a compass.\n", + "\n", + "In the following we will calculate some metrics to numerically describe the quality of the implemented approach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task: Calculation of metrics for the evaluation of localization quality" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will start with the calculation of the most basic metrics: The distance error between estimate and ground truth in `x`, `y` and `psi`.\n", + "\n", + "Pandas allows us to add new fields to the data frame based on calculations in a very intuitive way. Use the example below to add the corresponding columns named `dx`, `dy` and `dpsi` to the data frame.\n", + "\n", + "#### Example:\n", + "`example_data_frame['C'] = example_data_frame['A'] + example_data_frame['B']`\n", + "- In this example `A` and `B` are existing fields in the data frame, the new field with name `C` will be added." + ] + }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "### START CODE HERE ###\n", + "\n", + "\n", + "\n", + "### Solution\n", "df['dx'] = df['x_ground_truth'] - df['x_estimate']\n", "df['dy'] = df['y_ground_truth'] - df['y_estimate']\n", - "df['dpsi'] = df['psi_ground_truth'] - df['psi_estimate']" + "df['dpsi'] = df['psi_ground_truth'] - df['psi_estimate']\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,3,figsize=(20, 4))\n", + "df.boxplot(column='dx', ax=ax[0])\n", + "df.boxplot(column='dy', ax=ax[1])\n", + "df.boxplot(column='dpsi', ax=ax[2])\n", + "plt.show()" ] }, { @@ -396,22 +457,9 @@ }, { "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "colormap = plt.get_cmap('viridis')\n", "normalize = Normalize(vmin=0.0, vmax=5.0)\n",