机器人SLAM建图与自主导航：从基础到实践

前言

这篇文章我开始和大家一起探讨机器人SLAM建图与自主导航，在前面的内容中，我们介绍了差速轮式机器人的概念及应用，谈到了使用Gazebo平台搭建仿真环境的教程，主要是利用gmapping slam算法，生成一张二维的仿真环境地图。我们也会在这篇文章中继续介绍并使用这片二维的仿真环境地图，用于我们的演示。

教程

SLAM算法的引入

（1）SLAM：Simultaneous Localization and Mapping，中文是即时定位与地图构建，所谓的SLAM算法准确说是能实现SLAM功能的算法，而不是某一个具体算法。

（2）现在各种机器人研发和商用化非常火，所有的自主机器人都绕不开一个问题，即在陌生环境中，需要知道周边是啥样（建图），需要知道我在哪（定位），于是有了SLAM 课题的研究。SLAM在室内机器人，自动驾驶汽车建图，VR/AR穿戴等领域都有广泛的应用。

（3）SLAM算法根据依赖的传感器不同，可以分为激光SLAM和视觉SLAM，前者是激光雷达，后者是能提供深度信息的摄像头，如双目摄像头，红外摄像头等。除此之外，SLAM算法通常还依赖里程计提供距离信息，否则地图很难无缝的拼接起来，很容易跑飞。一个经典的SLAM 流程框架如下，其中回环检测时为了判断机器人有没有来过之前的位置。

整体视觉SLAM的流程图

gmapping算法的基本原理

（1）现在ROS里有一系列SLAM算法包，如：gmapping ，hector（不需要里程计，比较特别），谷歌开源的cartographer（效率高），rtabmap（前面是二维的，这是三维建图）等。

（2）gmapping是基于激光雷达的，需要里程计信息，创建二维格栅地图。其中IMU信息可以没有。

（3）ros中激光雷达数据消息是 sensor_msgs/LaserScan ，内容如下：

（4）ros中里程计数据消息是 nav_msgs/Odometry 。

（5）gmapping 发布的地图meta数据：

（6）gmapping 发布的地图栅格数据

mbot_navigation

（1）ubuntu20.04 + ros noetic下，安装gmapping和保存地图文件的map_server

sudo apt-get install ros-noetic-gmapping
sudo apt-get install ros-noetic-map-server
// 补充：这是安装hector
sudo apt-get install ros-noetic-hector-slam

（2）创建 mbot_navigation 和相关文件

cd ~/catkin_ws/src
catkin_create_pkg mbot_navigation geometry_msgs move_base_msgs actionlib roscpp rospycd mbot_navigation 
mkdir launch maps rviz
touch launch/gmapping.launch

（3）调用gmapping算法，只需要写launch文件就行了，不用编码。gmapping.launch

<launch>// mbot_gazebo 会通过发/scan topic，传出lidar数据<arg name="scan_topic" default="scan" />// gammping一大堆参数，这里都是从他的demo里扣出来的，不用改。// 如果想用的好，可以尝试修改，甚至改一些代码，这就是算法（调参）工程师！<node pkg="gmapping" type="slam_gmapping" name="slam_gmapping" output="screen" clear_params="true">// mbot_gazebo 会通过发/odom topic，传出里程计数据<param name="odom_frame" value="odom"/><param name="map_update_interval" value="5.0"/><!-- Set maxUrange < actual maximum range of the Laser --><param name="maxRange" value="5.0"/><param name="maxUrange" value="4.5"/><param name="sigma" value="0.05"/><param name="kernelSize" value="1"/><param name="lstep" value="0.05"/><param name="astep" value="0.05"/><param name="iterations" value="5"/><param name="lsigma" value="0.075"/><param name="ogain" value="3.0"/><param name="lskip" value="0"/><param name="srr" value="0.01"/><param name="srt" value="0.02"/><param name="str" value="0.01"/><param name="stt" value="0.02"/><param name="linearUpdate" value="0.5"/><param name="angularUpdate" value="0.436"/><param name="temporalUpdate" value="-1.0"/><param name="resampleThreshold" value="0.5"/><param name="particles" value="80"/><param name="xmin" value="-1.0"/><param name="ymin" value="-1.0"/><param name="xmax" value="1.0"/><param name="ymax" value="1.0"/><param name="delta" value="0.05"/><param name="llsamplerange" value="0.01"/><param name="llsamplestep" value="0.01"/><param name="lasamplerange" value="0.005"/><param name="lasamplestep" value="0.005"/><remap from="scan" to="$(arg scan_topic)"/></node>// 保存的rviz配置文件<node pkg="rviz" type="rviz" name="rviz" args="-d $(find mbot_navigation)/rviz/map.rviz"/>
</launch>

（4）连同mbot_gazebo，一起编译运行

cd ~/catkin_ws
catkin_make -DCATKIN_WHITELIST_PACKAGES="mbot_navigation;mbot_gazebo"
source devel/setup.bash
// 打开仿真环境
roslaunch mbot_gazebo mbot_gazebo.launch
//再开一个窗口，打开gmapping
roslaunch mbot_navigation gmapping.launch
// 控制机器人行动，进行建图
roslaunch mbot_gazebo mbot_teletop.launch
// 建图完成后，新开窗口，执行map_server，保存生成的地图
cd ~/catkin_ws/src/mbot_navigation/maps
rosrun map_server map_saver -f gmapping_save

最终保存下来的地图

总结

在github上面的访问地址：https://github.com/Jieshoudaxue/ros_senior/tree/main/mbot_navigation/config/move_base

代码示例：

#include <ros/ros.h>
#include <list>
#include <geometry_msgs/Pose.h>
#include <move_base_msgs/MoveBaseAction.h>
#include <actionlib/client/simple_action_client.h>geometry_msgs::Pose createPose(double px, double py, double pz, double ox, double oy, double oz, double ow) {geometry_msgs::Pose pose;pose.position.x = px;pose.position.y = py;pose.position.z = pz;pose.orientation.x = ox;pose.orientation.y = oy;pose.orientation.z = oz;pose.orientation.w = ow;return pose;  
}int main(int argc, char** argv) {ros::init(argc, argv, "move_test");actionlib::SimpleActionClient<move_base_msgs::MoveBaseAction> move_base_client("move_base", true);ROS_INFO("Waiting for move_base action server...");  move_base_client.waitForServer();ROS_INFO("connected to move base server");std::vector<geometry_msgs::Pose> target_list;target_list.push_back(createPose(6.543, 4.779, 0.000, 0.000, 0.000, 0.645, 0.764));target_list.push_back(createPose(5.543, -4.779, 0.000, 0.000, 0.000, 0.645, 0.764));target_list.push_back(createPose(-5.543, 4.779, 0.000, 0.000, 0.000, 0.645, 0.764));target_list.push_back(createPose(-5.543, -4.779, 0.000, 0.000, 0.000, 0.645, 0.764));for (uint8_t i = 0; i < target_list.size(); i ++) {ros::Time start_time = ros::Time::now();ROS_INFO("going to %u goal, position: (%f, %f)", i, target_list[i].position.x, target_list[i].position.y);move_base_msgs::MoveBaseGoal goal;goal.target_pose.header.frame_id = "map";goal.target_pose.header.stamp = ros::Time::now();goal.target_pose.pose = target_list[i];move_base_client.sendGoal(goal);move_base_client.waitForResult();if (move_base_client.getState() == actionlib::SimpleClientGoalState::SUCCEEDED) {ros::Duration running_time =  ros::Time::now() - start_time;ROS_INFO("go to %u goal succeeded, running time %f sec", i, running_time.toSec());} else {ROS_INFO("goal failed");}}return 0;
}

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
YOLO-specific modulesUsage:$ python path/to/models/yolo.py --cfg yolov5s.yaml
"""import argparse
import sys
from copy import deepcopy
from pathlib import PathFILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd())  # relativefrom models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
from utils.plots import feature_visualization
from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_synctry:import thop  # for FLOPs computation
except ImportError:thop = Noneclass Detect(nn.Module):stride = None  # strides computed during buildonnx_dynamic = False  # ONNX export parameterdef __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layersuper().__init__()self.nc = nc  # number of classesself.no = nc + 5  # number of outputs per anchorself.nl = len(anchors)  # number of detection layersself.na = len(anchors[0]) // 2  # number of anchorsself.grid = [torch.zeros(1)] * self.nl  # init gridself.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor gridself.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output convself.inplace = inplace  # use in-place ops (e.g. slice assignment)def forward(self, x):z = []  # inference outputfor i in range(self.nl):x[i] = self.m[i](x[i])  # convbs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()if not self.training:  # inferenceif self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)y = x[i].sigmoid()if self.inplace:y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xyy[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # whelse:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xywh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # why = torch.cat((xy, wh, y[..., 4:]), -1)z.append(y.view(bs, -1, self.no))return x if self.training else (torch.cat(z, 1), x)def _make_grid(self, nx=20, ny=20, i=0):d = self.anchors[i].deviceif check_version(torch.__version__, '1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibilityyv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)], indexing='ij')else:yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)])grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()anchor_grid = (self.anchors[i].clone() * self.stride[i]) \.view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()return grid, anchor_gridclass Model(nn.Module):def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classessuper().__init__()if isinstance(cfg, dict):self.yaml = cfg  # model dictelse:  # is *.yamlimport yaml  # for torch hubself.yaml_file = Path(cfg).namewith open(cfg, encoding='ascii', errors='ignore') as f:self.yaml = yaml.safe_load(f)  # model dict# Define modelch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channelsif nc and nc != self.yaml['nc']:LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")self.yaml['nc'] = nc  # override yaml valueif anchors:LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')self.yaml['anchors'] = round(anchors)  # override yaml valueself.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelistself.names = [str(i) for i in range(self.yaml['nc'])]  # default namesself.inplace = self.yaml.get('inplace', True)# Build strides, anchorsm = self.model[-1]  # Detect()if isinstance(m, Detect):s = 256  # 2x min stridem.inplace = self.inplacem.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forwardm.anchors /= m.stride.view(-1, 1, 1)check_anchor_order(m)self.stride = m.strideself._initialize_biases()  # only run once# Init weights, biasesinitialize_weights(self)self.info()LOGGER.info('')def forward(self, x, augment=False, profile=False, visualize=False):if augment:return self._forward_augment(x)  # augmented inference, Nonereturn self._forward_once(x, profile, visualize)  # single-scale inference, traindef _forward_augment(self, x):img_size = x.shape[-2:]  # height, widths = [1, 0.83, 0.67]  # scalesf = [None, 3, None]  # flips (2-ud, 3-lr)y = []  # outputsfor si, fi in zip(s, f):xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))yi = self._forward_once(xi)[0]  # forward# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # saveyi = self._descale_pred(yi, fi, si, img_size)y.append(yi)y = self._clip_augmented(y)  # clip augmented tailsreturn torch.cat(y, 1), None  # augmented inference, traindef _forward_once(self, x, profile=False, visualize=False):y, dt = [], []  # outputsfor m in self.model:if m.f != -1:  # if not from previous layerx = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layersif profile:self._profile_one_layer(m, x, dt)x = m(x)  # runy.append(x if m.i in self.save else None)  # save outputif visualize:feature_visualization(x, m.type, m.i, save_dir=visualize)return xdef _descale_pred(self, p, flips, scale, img_size):# de-scale predictions following augmented inference (inverse operation)if self.inplace:p[..., :4] /= scale  # de-scaleif flips == 2:p[..., 1] = img_size[0] - p[..., 1]  # de-flip udelif flips == 3:p[..., 0] = img_size[1] - p[..., 0]  # de-flip lrelse:x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scaleif flips == 2:y = img_size[0] - y  # de-flip udelif flips == 3:x = img_size[1] - x  # de-flip lrp = torch.cat((x, y, wh, p[..., 4:]), -1)return pdef _clip_augmented(self, y):# Clip YOLOv5 augmented inference tailsnl = self.model[-1].nl  # number of detection layers (P3-P5)g = sum(4 ** x for x in range(nl))  # grid pointse = 1  # exclude layer counti = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indicesy[0] = y[0][:, :-i]  # largei = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indicesy[-1] = y[-1][:, i:]  # smallreturn ydef _profile_one_layer(self, m, x, dt):c = isinstance(m, Detect)  # is final layer, copy input as inplace fixo = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPst = time_sync()for _ in range(10):m(x.copy() if c else x)dt.append((time_sync() - t) * 100)if m == self.model[0]:LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')if c:LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency# https://arxiv.org/abs/1708.02002 section 3.3# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.m = self.model[-1]  # Detect() modulefor mi, s in zip(m.m, m.stride):  # fromb = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # clsmi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)def _print_biases(self):m = self.model[-1]  # Detect() modulefor mi in m.m:  # fromb = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)LOGGER.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))# def _print_weights(self):#     for m in self.model.modules():#         if type(m) is Bottleneck:#             LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weightsdef fuse(self):  # fuse model Conv2d() + BatchNorm2d() layersLOGGER.info('Fusing layers... ')for m in self.model.modules():if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update convdelattr(m, 'bn')  # remove batchnormm.forward = m.forward_fuse  # update forwardself.info()return selfdef info(self, verbose=False, img_size=640):  # print model informationmodel_info(self, verbose, img_size)def _apply(self, fn):# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffersself = super()._apply(fn)m = self.model[-1]  # Detect()if isinstance(m, Detect):m.stride = fn(m.stride)m.grid = list(map(fn, m.grid))if isinstance(m.anchor_grid, list):m.anchor_grid = list(map(fn, m.anchor_grid))return selfdef parse_model(d, ch):  # model_dict, input_channels(3)LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchorsno = na * (nc + 5)  # number of outputs = anchors * (classes + 5)layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, argsm = eval(m) if isinstance(m, str) else m  # eval stringsfor j, a in enumerate(args):try:args[j] = eval(a) if isinstance(a, str) else a  # eval stringsexcept NameError:passn = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gainif m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:c1, c2 = ch[f], args[0]if c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost]:args.insert(2, n)  # number of repeatsn = 1elif m is nn.BatchNorm2d:args = [ch[f]]elif m is Concat:c2 = sum(ch[x] for x in f)elif m is Detect:args.append([ch[x] for x in f])if isinstance(args[1], int):  # number of anchorsargs[1] = [list(range(args[1] * 2))] * len(f)elif m is Contract:c2 = ch[f] * args[0] ** 2elif m is Expand:c2 = ch[f] // args[0] ** 2else:c2 = ch[f]m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # modulet = str(m)[8:-2].replace('__main__.', '')  # module typenp = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number paramsLOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # printsave.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelistlayers.append(m_)if i == 0:ch = []ch.append(c2)return nn.Sequential(*layers), sorted(save)if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--profile', action='store_true', help='profile model speed')parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')opt = parser.parse_args()opt.cfg = check_yaml(opt.cfg)  # check YAMLprint_args(FILE.stem, opt)device = select_device(opt.device)# Create modelmodel = Model(opt.cfg).to(device)model.train()# Profileif opt.profile:img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)y = model(img, profile=True)# Test all modelsif opt.test:for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):try:_ = Model(cfg)except Exception as e:print(f'Error in {cfg}: {e}')# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)# from torch.utils.tensorboard import SummaryWriter# tb_writer = SummaryWriter('.')# LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph