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Fixed colab link, set some cells to hide, added Colab env check
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‎python/gtsam/examples/StereoVOExample.ipynb‎

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@@ -14,16 +14,22 @@
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"3. From both poses, the robot observes three landmarks (`L(1)`, `L(2)`, `L(3)`) with a stereo camera.\n",
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"\n",
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"We will:\n",
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"* Define camera calibration and noise models.\n",
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"* Create a factor graph representing the problem.\n",
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"* Provide initial estimates for poses and landmark positions.\n",
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"* Optimize the graph using Levenberg-Marquardt to find the most probable configuration.\n"
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"- Define camera calibration and noise models.\n",
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"- Create a factor graph representing the problem.\n",
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"- Provide initial estimates for poses and landmark positions.\n",
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"- Optimize the graph using Levenberg-Marquardt to find the most probable configuration.\n",
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"\n",
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"**Note:** This example is also available in C++. If you're interested in the C++ implementation, refer to `StereoVOExample.cpp` in the [GTSAM examples directory](https://github.com/borglab/gtsam/tree/develop/examples)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "e33e553a",
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"metadata": {},
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"source": [
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"GTSAM Copyright 2010-2025, Georgia Tech Research Corporation,\n",
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"Atlanta, Georgia 30332-0415\n",
@@ -39,33 +45,49 @@
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"id": "6af0f339",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/gtsam-project/gtsam-examples/blob/main/python/StereoVOExample_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"<a href=\"https://colab.research.google.com/github/borglab/gtsam/blob/develop/python/gtsam/examples/StereoVOExample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "43bbbfe4",
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"metadata": {},
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"outputs": [],
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Not running in Google Colab.\n"
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]
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}
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],
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"source": [
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"# Install necessary libraries\n",
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"%pip install -q gtsam-develop\n",
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"%pip install -q numpy"
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"try:\n",
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" import google.colab\n",
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" print(\"Running in Google Colab, installing necessary libraries...\")\n",
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" %pip install --quiet gtsam-develop\n",
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" print(\"Done!\")\n",
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"except ImportError:\n",
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" print(\"Not running in Google Colab. Please install necessary libraries as needed.\")\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "ac25f0ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gtsam\n",
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"import numpy as np\n",
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"\n",
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"# For shorthand for common GTSAM types (like X for poses, L for landmarks)\n",
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"from gtsam.symbol_shorthand import X, L\n"
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"from gtsam.symbol_shorthand import X, L"
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]
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},
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{
@@ -82,10 +104,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "342a585f",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Graph size after adding prior: 1\n"
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]
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}
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],
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"source": [
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"# Create an empty nonlinear factor graph\n",
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"graph = gtsam.NonlinearFactorGraph()\n",
@@ -118,10 +148,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"id": "e04b26c7",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"K: 1000 0 320\n",
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" 0 1000 240\n",
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" 0 0 1\n",
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"Baseline: 0.2\n",
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"\n"
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]
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}
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],
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"source": [
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"# Create stereo camera calibration object\n",
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"K = gtsam.Cal3_S2Stereo(1000, 1000, 0, 320, 240, 0.2)\n",
@@ -153,10 +195,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"id": "e9778bec",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Graph size after adding X(1) factors: 4\n"
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]
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}
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],
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"source": [
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"# Factors from X(1) to landmarks\n",
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"# StereoPoint2(uL, uR, v)\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"id": "495fc44f",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Graph size after adding all factors: 7\n"
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]
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}
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],
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"source": [
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"# Factors from X(2) to landmarks\n",
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"graph.add(gtsam.GenericStereoFactor3D(\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "b4be6ab2",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "d0c3a87b",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 9,
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"id": "093fe3dc",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Initial Error: 3434.6236\n",
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"Final Error : 0.0000\n",
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"\n",
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"Optimized Pose X(1):\n",
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" R: [\n",
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"\t1, 0, 0;\n",
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"\t0, 1, 0;\n",
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"\t0, 0, 1\n",
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"]\n",
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"t: 0 0 0\n",
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"\n",
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"\n",
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"Optimized Pose X(2):\n",
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" R: [\n",
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"\t1, -3.03422e-17, -2.0943e-16;\n",
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"\t3.03422e-17, 1, 1.30264e-15;\n",
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"\t2.0943e-16, -1.30264e-15, 1\n",
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"]\n",
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"t: 6.11092e-13 -6.15462e-13 1\n",
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"\n",
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"Translation component of X(2): [ 6.11092279e-13 -6.15461900e-13 1.00000000e+00]\n",
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"\n",
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"Optimized Landmark (L(1)):\n",
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" [1. 1. 5.]\n",
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"\n",
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"Optimized Landmark (L(2)):\n",
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" [-1. 1. 5.]\n",
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"\n",
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"Optimized Landmark (L(3)):\n",
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" [-1.21564099e-16 -5.00000000e-01 5.00000000e+00]\n"
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]
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}
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],
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"source": [
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"# print(result)\n",
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"print(f\"Initial Error: {graph.error(initial_estimate):.4f}\")\n",

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